A unified framework for machine learning with time series

Last update: Jan 6, 2023

Overview

Welcome to sktime

A unified framework for machine learning with time series

We provide specialized time series algorithms and scikit-learn compatible tools to build, tune and validate time series models for multiple learning problems, including:

Forecasting,
Time series classification,
Time series regression.

For deep learning, see our companion package: sktime-dl.

CI
Docs
Community
Code

Installation

The package is available via PyPI using:

pip install sktime

Alternatively, you can install it via conda:

conda install -c conda-forge sktime

The package is actively being developed and some features may not be stable yet.

Development version

To install the development version, please see our advanced installation instructions.

Quickstart

Forecasting

from sktime.forecasting.all import *

y = load_airline()
y_train, y_test = temporal_train_test_split(y)
fh = ForecastingHorizon(y_test.index, is_relative=False)
forecaster = ThetaForecaster(sp=12)  # monthly seasonal periodicity
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
smape_loss(y_test, y_pred)
>>> 0.08661468139978168

For more, check out the forecasting tutorial.

Time Series Classification

from sktime.classification.all import *
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_arrow_head(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
classifier = TimeSeriesForest()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)
>>> 0.8679245283018868

For more, check out the time series classification tutorial.

Documentation

PyData Amsterdam 2020 tutorial: [video], [notebooks]
Tutorial notebooks - you can run them on Binder without having to install anything!
User guide
API reference

How to contribute

We follow the all-contributors specification - and all kinds of contributions are welcome!

Contributing guide
Enhancement proposals (design discussions)

If you have a question, chat with us or raise an issue. Your help and feedback is extremely welcome!

Development roadmap

Multivariate/panel forecasting,
Time series clustering,
Time series annotation (segmentation and anomaly detection),
Probabilistic time series modelling, including survival and point processes.

Read our detailed roadmap here.

How to cite sktime

If you use sktime in a scientific publication, we would appreciate citations to the following paper:

Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz Király (2019): “sktime: A Unified Interface for Machine Learning with Time Series”

Bibtex entry:

@inproceedings{sktime,
    author = {L{\"{o}}ning, Markus and Bagnall, Anthony and Ganesh, Sajaysurya and Kazakov, Viktor and Lines, Jason and Kir{\'{a}}ly, Franz J},
    booktitle = {Workshop on Systems for ML at NeurIPS 2019},
    title = {{sktime: A Unified Interface for Machine Learning with Time Series}},
    date = {2019},
}

Comments

Pairwise transformers, kernels/distances on tabular data and panel data - base class, examples, extension templates
Related to #52 (oldest open issue!) and #1062.

This PR contains:

base classes for a new scitype: pairwise transformers, i.e., distance matrix makers and kernel matrix makers; there are two base classes for two different inputs, tabular (classical distances/kernels) and time series panels (e.g., time warping distances)

an example tabular distance ScipyDist that interfaces the scipy distances

an example panel distance AggrDist that uses any tabular distance to create an aggregate sample distance between time series, e.g., the mean Euclidean distance

extension templates for both scitypes

tests for the concrete scitypes added

This may be useful for future extension of the clustering module, or refactorings and extensions of the TSC module, @chrisholder, @TonyBagnall.

Also pinging @jasonlines since this is a proposal how the existing distances (due to @jasonlines) could be refactored.
opened by fkiraly 67
[ENH] Save/Load Functionality for both Classical and DL Estimators
Reference Issues/PRs

Fixes #3022

What does this implement/fix? Explain your changes.

The Save/Load function for classical estimators is from #3336 and has been expanded for DL models along the same design. Keras is known to be problematic for certain kinds of (de)serialization, so this uses model.save() from keras along with pickle.dump(). The estimators are saved as .zip files.

Components of this directory include:

Metadata

Object

Keras History

Keras Model subdirectory

The first two components are the same as previously mentioned PR, stored differently to avoid this problem.

All the attributes are saved and restored (optimizers/history etc.) appropriately.

Does your contribution introduce a new dependency? If yes, which one?

No

What should a reviewer concentrate their feedback on?

One thing in __getstate__() and __setstate__() might be a source of contention, I've explained why it was necessary and I believe it is the simplest solution.

Any other comments?

PR checklist

For all contributions

[x] The PR title starts with either [ENH], [MNT], [DOC], or [BUG] indicating whether the PR topic is related to enhancement, maintenance, documentation, or bug.

implementing framework
opened by achieveordie 59
Forecasting support for multivariate y and multiple input/output types - working prototype
This is a merge/review ready sketch for how multivariate y could be supported for multivariate forecasters, alongside the possibility to pass np.array and pd.Series. This is based on the design in STEP no.5, adapted to the new forecasting base class.

The key ingredient are:

converters parameterized by from, to, and as - in the convertIOmodule. Besides the obvious conversion functionality, the converters can be given access to a dictionary via reference in the store argument, where information for inverting lossy conversions (like from pd.DataFrame to np.array) can be stored

a new tag y_type which encodes the type of y that the private _fit, _predict, and _update assume internally - for now, it's just one type and not a list of compatible types

some logic in the public "plumbing" area of fit, predict, update, which converts inputs to the public layer to the desired input of the logic layer and back

This is for discussion, and I expect major change before we merge.

It might be especially interesting to discuss the following interaction with typing: the third argument of the converter, the "as" argument, is a scitype, and designed to have the same name as the respective typing composite type. Also see discussion in #965, #973 and #974.

Thinking of the logical continuation of this:

extend this to the other arguments, e.g., X

the individual argument checks like check_X etc should be replaced by checks of the kind check(what, as : scitype), e.g., check(y, "Series") should replace the less generic check_y.

extend this to time series classification, to support 3D numpy arrays, awkward arrays, nested data frames

A "visionary" pathway may see this working with type annotations that are scitypes, not typing machine types, e.g.,

fit(X : Series, Y : Series) etc _fit(X: Series, Y : UnivariateSeries)

and checks/conversions being done automatically.
module:forecasting
opened by fkiraly 56
[ENH] refactored `KNeighborsTimeSeriesClassifier`
This PR is a refactor of the KNeighborsTimeSeriesClassifier.

It does three things:

removes the (in my opinion) awkward and hard-to-maintain double inheritance of KNeighborsTimeSeriesClassifier from sklearn's knn classifier. Instead adopts a wrapper pattern around the sklearn public interface, with much of the code simplified through passing distance matrices directly to sklearn.

extends the interface to allow passing of arbitrary distances that are callables. This supports the composable distance interface from #1858 out-of-the-box (with sklearn style parameter inspectable composition/pipelining), and can be used to plug in custom time series distances.

tests that rely on random seeds are updated with new expected output values

This PR also fixes two unreported bugs:

the algorithm parameter of KNeighborsTimeSeriesClassifier was not passed to the sklearn KNeighbors classifier, the value being passed was always the default "brute". https://github.com/alan-turing-institute/sktime/issues/2774

corrected docstring in pairwise_distance which was missing options

and makes one minor change:

replaces loops in tests for KNeighborsTimeSeriesClassifier by mark.parametrize

module:classification
opened by fkiraly 54
[ENH] MLflow custom flavor for `sktime` forecasting
Reference Issues/PRs

Resolves #1215

What does this implement/fix? Explain your changes.

This PR adds mlflow custom model flavor for sktime. The implementation closely follows the approach of mlflow built-in model flavors, see pmdarima as an example in mlflow repo (for more details see mlflow.pmdarima and tests.pmdarima.test_pmdarima_model_export). I also added a mlflow notebook under examples which could be added to the sphinx docs.

Does your contribution introduce a new dependency? If yes, which one?

New dependencies introduced: boto3, mlflow, moto

I tried to add all dependencies as soft dependencies but ran into a challenge for mlflow. We need to import pyfunc from mlflow at the module level to enable loading sktime model as pyfunc flavor. This will not work if we import pyfunc inside the functions.

Would it be acceptable to have mlflow as core dependency or do you have another suggestion?

What should a reviewer concentrate their feedback on?

how we want to handle the open dependency question

check the _SktimeModelWrapperclass which contains the sktime custom inference logic

predict method of _SktimeModelWrapper is only allowed to take a single DataFrame as an input (see mlflow documentation for more details) - thus we can pass exogenous regressor only as 2D or 3D numpy array. Based on my understanding this would cover all forecasters utilizing scitypes Series and Panel - can you confirm this?

whether you see the need to add more unit tests

should we add example notebook to sphinx docs? Is there anything in the code that needs to be modified for this inclusion?

Any other comments?

PR checklist

For all contributions

[x] I've added myself to the list of contributors.

[x] Optionally, I've updated sktime's CODEOWNERS to receive notifications about future changes to these files.

[x] I've added unit tests and made sure they pass locally.

[x] The PR title starts with either [ENH], [MNT], [DOC], or [BUG] indicating whether the PR topic is related to enhancement, maintenance, documentation, or bug.

For new estimators

[ ] I've added the estimator to the online documentation.

[ ] I've updated the existing example notebooks or provided a new one to showcase how my estimator works.

module:forecasting
opened by benjaminbluhm 52
CI/CD refactor and switch to github actions
Reference Issues/PRs

Fixes #1482

Fixes #1728

Fixes #1803

Fixes #1832

See also #1150 Refactoring of github actions workflows started in #1321

Currently blocked by:

[x] #1644

[x] #1661

[x] #1662

[x] #1792

[x] #1747

[x] #1783

[x] #1803

[x] update installation instructions in docs/source/installation.rst

What does this implement/fix? Explain your changes.

This is a rather substantial effort involving changes to how sktime is build and distributed. The main changes are:

Add pyproject.toml (see PEP 621 and PEP 633]. The intention behind is to collect all package matadata and dependencies in one place.

Add sets of optional dependencies:

all_extras all "soft" dependencies

dev packages useful for development and testing

binder packages required for binder and running notebook examples

docs packages required to build the documentation

Add section to build time dependencies.

Refactor existing CI/CD pipelines to use the new build configuration.

Introduced a new CI/CD pipelines for running tests across win, mac, linux and py37, py38, py39 with github actions.

Removed the obsolete requirements files.

Include wheels build for macosx on arm64.

Removed

❌ Drop support for python3.6

❌ Drop appveyor and azure pipelines

High level diagram for the CI/CD workflows

Does your contribution introduce a new dependency? If yes, which one?

It does not introduce any new runtime/install dependencies but it could introduce an optional development depencency/build system such as poetry to simplify development/build/distribution.

What should a reviewer concentrate their feedback on?

This PR introduces changed to the CI/CD pipelines that would hopefully allow to drop appveyor and azure pipelines in favor of github actions which would simplify the current build and maintenance process.

Any other comments?

PR checklist

For all contributions

[x] I've added myself to the list of contributors.

[x] Optionally, I've updated sktime's CODEOWNERS to receive notifications about future changes to these files.

[x] I've added unit tests and made sure they pass locally.
opened by lmmentel 48
[ENH] Early classification base class

Implementation of a base class and converted tests for early classification https://github.com/alan-turing-institute/sktime/issues/1616. Refactors TEASER to use the new base class.

The new class is very similar to the classification base class, except predict/predict_proba returns 3 ndarrays instead of 1. The first is the already present predictions/probabilities, the second is a set of decisions on whether the returned predictions are safe to use, and the third is a set of state information for each case for use in later decisions and putting together eTSC statistics.

An optional state_info input for the predict/predict_proba is also required.
module:classification

opened by MatthewMiddlehurst 46
[MNT] Remove hcrystalball dependency

Reference Issues/PRs

#2675

What does this implement/fix? Explain your changes.

Remove hcrystalball. To be merged before release v0.13.0 and after v0.12.0
maintenance module:forecasting

opened by aiwalter 39
Automated learning algorithm overview
Contributors: @fkiraly, @mloning, @afzal442, @abdulelahsm, @sparkingdark

idea:

web data base for learning algorithms with fields for:

class name,

scitype (e.g. forecaster, classifier, etc.),

original contributor,

maintainer/current code owner,

link to API reference,

optional additional fields:

links to literature references,

package dependency,

code health/status (under development, mature, etc),

link to example notebooks

use cases:

search for suitable algorithms given a problem

search for information about a given algorithm

reference example from mlr3 library:

https://mlr3extralearners.mlr-org.com/articles/learners/learner_status.html

https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html

GitHub action to automatically generate table: https://github.com/mlr-org/mlr3extralearners/blob/main/.github/workflows/update_learner_table.yml

related code in sktime:

from sktime.utils import all_estimators all_estimators(estimator_types="classifier")

https://github.com/alan-turing-institute/sktime/blob/ee7a06843a44f4aaec7582d847e36073a9ab0566/sktime/utils/init.py#L16

questions:

integrate it with API reference: https://www.sktime.org/en/latest/api_reference.html (adding columns to that table)?

notes:

make clear that not all algorithm are reference implementations, even though we link them with the original research paper, some may be re-implemented, adapted or interfaced from other packages

encourage people to ping algorithm maintainers directly when they have questions about algorithm using the GitHub user name

Adding col for maintainer using GH

documentation
opened by mloning 38
Add Clasp for time series segmentation (CIKM'21 publication)

Reference Issues/PRs

#1346

What does this implement/fix? Explain your changes.

This request implements ClaSP, a novel time series segmentation method published at CIKM '21.

The segmentation is implemented as an annotator, and the profile in ClaSP is implemented as a SeriesToSeriesTransformer.
implementing algorithms

opened by patrickzib 37
Refactor tags
Is your feature request related to a problem? Please describe.

use of "capabilities" rather than "tags" in classification module

unclear naming of _all_tags method

inconsistent tags for composition algorithms (see #977)

integration in all_estimators function to make them usable in finding/filtering models

Describe the solution you'd like

refactor capabilities into tags

rename _all_tags to get_tags and make public

add tag kwarg to all_estimators

add tests to catch inconsistent tags

API design
opened by mloning 37
[BUG] `VAR` fails estimator tests if `ic="aic"`
VAR fails the check_estimator tests when ic="aic" is set.

To reproduce:

from sktime.forecasting.var import VAR from sktime.utils.estimator_checks import check_estimator f = VAR(ic="aic") check_estimator(f, return_exceptions=False)

Unclear why, I noticed via https://github.com/sktime/sktime/pull/4043
bug module:forecasting
opened by fkiraly 0
[ENH] rolling window (out of sample) detrender

It would be good to have a rolling window detrender that allows out-of-sample forecast detrending, i.e., computing rolling window out-of-sample residuals rather than in-sample residuals.

The current Detrender computes in-sample residuals, when naively applied (via fit_transform, which is called in a forecasting pipeline).

Not sure whether this should be an option in the class or a separate class - it should probably take a splitter (inheriting from BaseSplitter) as an argument. There is also some thought to be had around what should happen if the series seen in transform is not entirely identical nor entirely different from the one in fit.

If someone wants to take this up soon, it is perhaps best to base this on the simplified logic in https://github.com/sktime/sktime/pull/4053 rather than the current main.
feature request good first issue module:forecasting module:transformations enhancement

opened by fkiraly 0
[ENH][BUG] clean up `Detrender` and implement correct multivariate behaviour

This PR fixes https://github.com/sktime/sktime/issues/4051 and also simplifies the logic in the Detrender.

As the BaseForecasting interface supports loop over hierarchy instances and variables natively, all the case distinctions internally can go, and with it goes the incorrect multivariate implementation.
module:forecasting module:transformations bugfix enhancement

opened by fkiraly 1
[ENH] Adding `min_periods` in `WindowSummarizer` to avoid different behaviour in the future

Reference Issues/PRs

Fixes #4050

What does this implement/fix? Explain your changes.

In the WindowSummarizer class, min_periods is explicitly defined in the pandas rolling function call with the value equal to the window_length.

Does your contribution introduce a new dependency? If yes, which one?

No.

opened by arnavrneo 2
[BUG] incorrect multivariate detrending in `Detrender`

Currently, the Detrender transformer implements multivariate detrending incorrectly, and in contrast to what its docstring says.

The difference between "should" and "is" happens if the forecaster passed is genuinely multivariate - in that case, the Detrender coerces it to univariate, and fits an univariate version per variable, instead of just applying the multivariate forecaster and detrending via it.

This has the status of a bug since the docstring does not agree with content, and the content is contrary to user expectation.
module:forecasting module:transformations enhancement

opened by fkiraly 0
[ENH] Explicitly specify `min_periods` in `WindowSummarizer` to avoid different behaviour in the future
Is your feature request related to a problem? Please describe.

Yes. As noticed by @KishManani in the slack, using pandas 1.5.2 for rolling operations (e.g. df.rolling(window=window_size).mean() ) throws a future warning:

FutureWarning: min_periods=None will default to the size of window consistent with other methods in a future version. Specify min_periods=0 instead.

So, it means that the behaviour of WindowSummarizer transformer (which uses rolling and doesn't specify min_periods at the moment) will change if in future pandas changes the default behaviour to be min_periods=0.

Describe the solution you'd like Explicitly specifying min_period with the value of window length can make WindowSummarizer to retain it's behaviour even after pandas changes the default.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.
enhancement
opened by arnavrneo 0

Releases(v0.15.0)

v0.15.0(Dec 22, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@nilesh05apr made their first contribution in https://github.com/sktime/sktime/pull/3881

@GianFree made their first contribution in https://github.com/sktime/sktime/pull/3945

@dsanr made their first contribution in https://github.com/sktime/sktime/pull/3887

@aquemy made their first contribution in https://github.com/sktime/sktime/pull/3915

@nshahpazov made their first contribution in https://github.com/sktime/sktime/pull/3878

@benjaminbluhm made their first contribution in https://github.com/sktime/sktime/pull/3912

@solen0id made their first contribution in https://github.com/sktime/sktime/pull/3714

@badrmarani made their first contribution in https://github.com/sktime/sktime/pull/3968

All Contributors

@aiwalter, @aquemy, @badrmarani, @benjaminbluhm, @bethrice44, @chillerobscuro, @ciaran-g, @danbartl, @dsanr, @fkiraly, @GianFree, @KatieBuc, @kcc-lion, @KishManani, @lmmentel, @nilesh05apr, @nshahpazov, @solen0id, @templierw, @TonyBagnall

Full Changelog: https://github.com/sktime/sktime/compare/v0.14.1...v0.15.0
Source code(tar.gz)
Source code(zip)
v0.14.1(Nov 30, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@matt-wisdom made their first contribution in https://github.com/sktime/sktime/pull/3759

@hadifawaz1999 made their first contribution in https://github.com/sktime/sktime/pull/3747

@jorenham made their first contribution in https://github.com/sktime/sktime/pull/3723

@wolph made their first contribution in https://github.com/sktime/sktime/pull/3723

@RikStarmans made their first contribution in https://github.com/sktime/sktime/pull/3768

@templierw made their first contribution in https://github.com/sktime/sktime/pull/3805

@michaelfeil made their first contribution in https://github.com/sktime/sktime/pull/3786

All Contributors

@achieveordie, @aiwalter, @ciaran-g, @danbartl, @eenticott-shell, @fkiraly, @hadifawaz1999, @haskarb, @hoesler, @jorenham, @KishManani, @lmmentel, @matt-wisdom, @MatthewMiddlehurst, @michaelfeil, @RikStarmans, @templierw, @TonyBagnall, @wolph

Full Changelog: https://github.com/sktime/sktime/compare/v0.14.0...v0.14.1
Source code(tar.gz)
Source code(zip)
v0.14.0(Nov 7, 2022)

What's new

Please see our changelog for a description of all changes.

All Contributors

@achieveordie, @aiwalter, @AnH0ang, @arampuria19, @bethrice44, @canbooo, @chillerobscuro, @chrisholder, @ciaran-g, @danbartl, @fkiraly, @GuzalBulatova, @kcc-lion, @khrapovs, @lmmentel, @MatthewMiddlehurst, @miraep8, @shagn, @TonyBagnall, @topher-lo

Full Changelog: https://github.com/sktime/sktime/compare/v0.13.4...v0.14.0
Source code(tar.gz)
Source code(zip)
v0.13.4(Sep 27, 2022)

Maintenance release - moves sktime repository to sktime org from alan-turing-institute org.

Incremental to 0.13.3. Please see our changelog for a description of all changes.
Source code(tar.gz)
Source code(zip)
v0.13.3(Sep 26, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@stepinski made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1940

@bugslayer-332 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3340

@shagn made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3400

@topher-lo made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3449

All Contributors

@achieveordie, @aiwalter, @AurumnPegasus, @benHeid, @bethrice44, @bugslayer-332, @fkiraly, @ilkersigirci, @KatieBuc, @khrapovs, @lmmentel, @ltsaprounis, @miraep8, @patrickzib, @paulbauriegel, @shagn, @stepinski, @topher-lo

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.13.2...v0.13.3
Source code(tar.gz)
Source code(zip)
v0.13.2(Aug 24, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@KishManani made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3257

@paulbauriegel made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3303

@chillerobscuro made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3317

All Contributors

@achieveordie, @aiwalter, @chillerobscuro, @chrisholder, @fkiraly, @haskarb, @KatieBuc, @kcc-lion, @KishManani, @miraep8, @patrickzib, @paulbauriegel

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.13.1...v0.13.2
Source code(tar.gz)
Source code(zip)
v0.13.1(Aug 10, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@kcc-lion made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3008

@soma2000-lang made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3060

@shchur made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3070

@jelc53 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3086

@Arvind644 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3066

@C-mmon made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3111

@mariamjabara made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2995

@achieveordie made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3109

@benHeid made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3167

@AnH0ang made their first contribution in https://github.com/alan-turing-institute/sktime/pull/3208

All Contributors

@achieveordie, @aiwalter, @AnH0ang, @Arvind644, @AurumnPegasus, @benHeid, @bethrice44, @C-mmon, @ciaran-g, @danbartl, @eenticott-shell, @fkiraly, @jelc53, @kcc-lion, @khrapovs, @lbventura, @lmmentel, @Lovkush-A, @ltsaprounis, @mariamjabara, @MatthewMiddlehurst, @miraep8, @shchur, @soma2000-lang, @sveameyer13, @TNTran92, @tobiasweede, @ZiyaoWei

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.13.0...v0.13.1
Source code(tar.gz)
Source code(zip)
v0.13.0(Jul 14, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@tobiasweede made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2882

@jlopezpena made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2979

All Contributors

@a-pasos-ruiz, @aiwalter, @AurumnPegasus, @ciaran-g, @fkiraly, @haskarb, @jlopezpena, @KatieBuc, @khrapovs, @lbventura, @lmmentel, @ltsaprounis, @MatthewMiddlehurst, @ris-bali, @TNTran92, @tobiasweede, @TonyBagnall

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v.0.12.1...v.0.13.0
Source code(tar.gz)
Source code(zip)
v.0.12.1(Jun 28, 2022)
What's new

Please see our changelog for a description of all changes.

New Contributors

@AurumnPegasus made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2786

All Contributors

@aiwalter, @AurumnPegasus, @bethrice44, @ciaran-g, @fkiraly, @khrapovs, @miraep8

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.12.0...v0.12.1
Source code(tar.gz)
Source code(zip)
v0.12.0(Jun 13, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@keepersas made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2676

@asattiraju13 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2688

@dougollerenshaw made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2722

@lbventura made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2648

@bethrice44 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2706

@ZiyaoWei made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2707

@DBCerigo made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2795

All Contributors

@aiwalter, @asattiraju13, @bethrice44, @chrisholder, @ciaran-g, @DBCerigo, @dougollerenshaw, @eenticott-shell, @fkiraly, @k1m190r, @keepersas, @khrapovs, @lbventura, @lielleravid, @ltsaprounis, @miraep8, @NoaBenAmi, @Ris-Bali, @TonyBagnall, @ZiyaoWei

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.11.4...v0.12.0
Source code(tar.gz)
Source code(zip)
v0.11.4(May 13, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@phershbe made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2630

All Contributors

@chrisholder, @ciaran-g, @fkiraly, @khrapovs, @miraep8, @phershbe, @Ris-Bali, @TonyBagnall

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.11.3...v0.11.4
Source code(tar.gz)
Source code(zip)
v0.11.3(Apr 30, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@tarpas made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2389

@miraep8 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2410

@Samuel-Oyeneye made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2503

@ksachdeva made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1822

@Ris-Bali made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2440

All Contributors

@aiwalter, @chrisholder, @ciaran-g, @danbartl, @eenticott-shell, @fkiraly, @GuzalBulatova, @juanitorduz, @KatieBuc, @khrapovs, @lmmentel, @ltsaprounis, @MatthewMiddlehurst, @miraep8, @ris-bali, @Samuel-Oyeneye, @tarpas, @Tomiiwa

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.11.2...v0.11.3
Source code(tar.gz)
Source code(zip)
v0.11.2(Apr 11, 2022)
What's Changed

🐛 Bug Fixes

[BUG] temp workaround for unnamed levels in hierarchical X passed to aggregator by @fkiraly in https://github.com/alan-turing-institute/sktime/pull/2432

[BUG] forecasting pipeline dunder fix by @fkiraly in https://github.com/alan-turing-institute/sktime/pull/2431

[BUG] fix erroneous direct passthrough in ColumnEnsembleForecaster by @fkiraly in https://github.com/alan-turing-institute/sktime/pull/2436

[BUG] Incorrect indices returned by make_reduction on hierarchical data fixed by @danbartl in https://github.com/alan-turing-institute/sktime/pull/2438

Other Changes

[MNT] Fix typo in PULL_REQUEST_TEMPLATE.md by @eltociear in https://github.com/alan-turing-institute/sktime/pull/2446

Release v0.11.2 by @lmmentel in https://github.com/alan-turing-institute/sktime/pull/2445

New Contributors

@eltociear made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2446

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.11.1...v0.11.2
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v0.11.1(Apr 10, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@0saurabh0 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2335

@TNTran92 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2304

@RISHIKESHAVAN made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2365

@NoaBenAmi made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2342

@KatieBuc made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2418

All Contributors

@0saurabh0, @aiwalter, @ciaran-g, @danbartl, @eenticott-shell, @fkiraly, @katiebuc, @khrapovs, @lmmentel, @ltsaprounis, @MatthewMiddlehurst, @NoaBenAmi, @RISHIKESHAVAN, @TNTran92

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.11.0...v0.11.1
Source code(tar.gz)
Source code(zip)
v0.11.0(Mar 26, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@mikofski made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2104

@theanorak made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2131

@RafaAyGar made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2135

@ciaran-g made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2069

@dionysisbacchus made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2166

@KishenSharma6 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2167

@hmtbgc made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2190

@cdahlin made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2193

@Vasudeva-bit made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2196

@FedericoGarza made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2251

@Tomiiwa made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2285

@lielleravid made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2277

All Contributors

@aiwalter, @cdahlin, @chrisholder, @ciaran-g, @danbartl, @dionysisbacchus, @eenticott-shell, @FedericoGarza, @fkiraly, @hmtbgc, @IlyasMoutawwakil, @kejsitake, @KishenSharma6, @lielleravid, @lmmentel, @ltsaprounis, @MatthewMiddlehurst, @mikofski, @RafaAyGar, @theanorak, @Tomiiwa, @TonyBagnall, @Vasudeva-bit

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.10.1...v0.11.0
Source code(tar.gz)
Source code(zip)
v0.10.1(Feb 20, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@schettino72 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1973

@Saransh-cpp made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2035

@Rubiel1 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2042

@chicken-biryani made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2014

@baggiponte made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1834

@RishiKumarRay made their first contribution in https://github.com/alan-turing-institute/sktime/pull/2063

All Contributors

All contributors: @aiwalter, @baggiponte, @chicken-biryani, @danbartl, @eenticott-shell, @fkiraly, @khrapovs, @lmmentel, @MatthewMiddlehurst, @rakshitha123, @RishiKumarRay, @Rubiel1, @Saransh-cpp, @schettino72,

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.10.0...v0.10.1
Source code(tar.gz)
Source code(zip)
v0.10.0(Feb 1, 2022)
What's New

Please see our changelog for a description of all changes.

New Contributors

@khrapovs made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1743

@eenticott-shell made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1672

@sumit-158 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1841

@ishannangia001 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1848

@shubhamkarande13 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1872

@k1m190r made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1842

@amrith-shell made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1707

@rakshitha123 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1901

All Contributors

All contributors: @aiwalter, @amrith-shell, @Aparna-Sakshi, @AreloTanoh, @chrisholder, @eenticott-shell, @fkiraly, @k1m190r, @kejsitake, @khrapovs, @lmmentel, @ltsaprounis, @MatthewMiddlehurst, @MrPr3ntice, @mloning, @ngupta23, @rakshitha123, @RNKuhns, @shubhamkarande13, @sumit-158, @TonyBagnall,

Full Changelog: https://github.com/alan-turing-institute/sktime/compare/v0.9.0...v0.10.0
Source code(tar.gz)
Source code(zip)
v0.9.0(Dec 8, 2021)
What's New

Please see our changelog for a description of all changes.

New Contributors

@OliverMatthews made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1588

@Carlosbogo made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1600

@chernika158 made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1615

@fstinner made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1567

@MrPr3ntice made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1636

@lmmentel made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1644

@AngelPone made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1641

@marcio55afr made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1671

All Contributors

All contributors: @Carlosbogo, @MatthewMiddlehurst, @OliverMatthews, @RNKuhns, @TonyBagnall, @fkiraly, @freddyaboulton and @mloning
Source code(tar.gz)
Source code(zip)
v0.8.1(Oct 28, 2021)
What's New

Please see our changelog for a description of all changes.

All contributors: @Aparna-Sakshi, @BINAYKUMAR943, @IlyasMoutawwakil, @MatthewMiddlehurst, @Piyush1729, @RNKuhns, @RavenRudi, @SveaMeyer13, @TonyBagnall, @afzal442, @aiwalter, @bobbys-dev, @boukepostma, @danbartl, @eyalshafran, @fkiraly, @freddyaboulton, @kejsitake, @mloning, @myprogrammerpersonality, @patrickzib, @ronnie-llamado, @xiaobenbenecho and @yairbeer

New Contributors

@ronnie-llamado made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1437

@bobbys-dev made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1453

@myprogrammerpersonality made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1464

@xiaobenbenecho made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1409

@yairbeer made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1475

@kejsitake made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1473

@boukepostma made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1493

@RavenRudi made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1487

@eyalshafran made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1489

@danbartl made their first contribution in https://github.com/alan-turing-institute/sktime/pull/1356

Source code(tar.gz)
Source code(zip)
v0.8.0(Sep 17, 2021)

What's New

Please see our changelog for a description of all changes.

All contributors: @Aparna-Sakshi, @AreloTanoh, @BINAYKUMAR943, @Flix6x, @GuzalBulatova, @IlyasMoutawwakil, @Lovkush-A, @MatthewMiddlehurst, @RNKuhns, @SveaMeyer13, @TonyBagnall, @afzal442, @aiwalter, @bilal-196, @corvusrabus, @fkiraly, @freddyaboulton, @juanitorduz, @justinshenk, @ltoniazzi, @mathco-wf, @mloning, @moradabaz, @pul95, @tensorflow-as-tf, @thayeylolu, @victordremov, @whackteachers and @xloem
Source code(tar.gz)
Source code(zip)
v0.7.0(Jul 13, 2021)
What's New

Added

new module (experimental): Time Series Clustering (#1049) @TonyBagnall

new module (experimental): Pairwise transformers, kernels/distances on tabular data and panel data - base class, examples, extension templates (#1071) @fkiraly @chrisholder

new module (experimental): Series annotation and PyOD adapter (#1021) @fkiraly @satya-pattnaik

Clustering extension templates, docstrings & get_fitted_params (#1100) @fkiraly

New Classifier: Implementation of signature based methods. (#714) @jambo6

New Forecaster: Croston's method (#730) @Riyabelle25

New Forecaster: ForecastingPipeline for pipelining with exog data (#967) @aiwalter

New Transformer: Multivariate Detrending (#1042) @SveaMeyer13

New Transformer: ThetaLines transformer (#923) @GuzalBulatova

sktime registry (#1067) @fkiraly

Feature/information criteria get_fitted_params (#942) @ltsaprounis

Add plot_correlations() to plot series and acf/pacf (#850) @RNKuhns

Add doc-quality tests on changed files (#752) @mloning

Docs: Create add_dataset.rst (#970) @Riyabelle25

Added two new related software packages (#1019) @aiwalter

Added orbit as related software (#1128) @aiwalter

adding fkiraly as codeowner for forecasting base classes (#989) @fkiraly

added mloning and aiwalter as forecasting/base code owners (#1108) @fkiraly

Changed

Update metric to handle y_train (#858) @RNKuhns

TSC base template refactor (#1026) @fkiraly

Forecasting refactor: base class refactor and extension template (#912) @fkiraly

Forecasting refactor: base/template docstring fixes, added fit_predict method (#1109) @fkiraly

Forecasters refactor: NaiveForecaster (#953) @fkiraly

Forecasters refactor: BaseGridSearch, ForecastingGridSearchCV, ForecastingRandomizedSearchCV (#1034) @GuzalBulatova

Forecasting refactor: polynomial trend forecaster (#1003) @thayeylolu

Forecasting refactor: Stacking, Multiplexer, Ensembler and TransformedTarget Forecasters (#977) @thayeylolu

Forecasting refactor: statsmodels and theta forecaster (#1029) @thayeylolu

Forecasting refactor: reducer (#1031) @Lovkush-A

Forecasting refactor: ensembler, online-ensembler-forecaster and descendants (#1015) @thayeylolu

Forecasting refactor: TbatAdapter (#1017) @thayeylolu

Forecasting refactor: PmdArimaAdapter (#1016) @thayeylolu

Forecasting refactor: Prophet (#1005) @thayeylolu

Forecasting refactor: CrystallBall Forecaster (#1004) @thayeylolu

Forecasting refactor: default tags in BaseForecaster; added some new tags (#1013) @fkiraly

Forecasting refactor: removing _SktimeForecaster and horizon mixins (#1088) @fkiraly

Forecasting tutorial rework (#972) @fkiraly

Added tuning tutorial to forecasting example notebook - fkiraly suggestions on top of #1047 (#1053) @fkiraly

Classification: Kernel based refactor (#875) @MatthewMiddlehurst

Classification: catch22 Remake (#864) @MatthewMiddlehurst

Forecasting: Remove step_length hyper-parameter from reduction classes (#900) @mloning

Transformers: Make OptionalPassthrough to support multivariate input (#1112) @aiwalter

Transformers: Improvement to Multivariate-Detrending (#1077) @SveaMeyer13

Update plot_series to handle pd.Int64 and pd.Range index uniformly (#892) @Dbhasin1

Including floating numbers as a window length (#827) @thayeylolu

update docs on loading data (#885) @SveaMeyer13

Update docs (#887) @mloning

[DOC] Updated docstrings to inform that methods accept ForecastingHorizon (#872) @julramos

Fixed

Fix use of seasonal periodicity in naive model with mean strategy (from PR #917) (#1124) @mloning

Fix ForecastingPipeline import (#1118) @mloning

Bugfix - forecasters should use internal interface _all_tags for self-inspection, not _has_tag (#1068) @fkiraly

bugfix: Prophet adapter fails to clone after setting parameters (#911) @Yard1

Fix seeding issue in Minirocket Classifier (#1094) @Lovkush-A

fixing soft dependencies link (#1035) @fkiraly

Fix minor typos in docstrings (#889) @GuzalBulatova

Fix manylinux CI (#914) @mloning

Add limits.h to ensure pip install on certain OS's (#915) @tombh

Fix side effect on input for Imputer and HampelFilter (#1089) @aiwalter

BaseCluster class issues resolved (#1075) @chrisholder

Cleanup metric docstrings and fix bug in _RelativeLossMixin (#999) @RNKuhns

minor clarifications in forecasting extension template preamble (#1069) @fkiraly

Fix fh in imputer method based on in-sample forecasts (#861) @julramos

Arsenal fix, extended capabilities and HC1 unit tests (#902) @MatthewMiddlehurst

minor bugfix - setting _is_fitted to False before input checks in forecasters (#941) @fkiraly

Properly process random_state when fitting Time Series Forest ensemble in parallel (#819) @kachayev

bump nbqa (#998) @MarcoGorelli

datetime: Construct Timedelta from parsed pandas frequency (#873) @ckastner

All contributors: @Dbhasin1, @GuzalBulatova, @Lovkush-A, @MarcoGorelli, @MatthewMiddlehurst, @RNKuhns, @Riyabelle25, @SveaMeyer13, @TonyBagnall, @Yard1, @aiwalter, @chrisholder, @ckastner, @fkiraly, @jambo6, @julramos, @kachayev, @ltsaprounis, @mloning, @thayeylolu and @tombh
Source code(tar.gz)
Source code(zip)
v0.6.1(May 14, 2021)
What's New

Fixed

Exclude Python 3.10 from manylinux CI (#870) @mloning

Fix AutoETS handling of infinite information criteria (#848) @ltsaprounis

Fix smape import (#851) @mloning

Changed

ThetaForecaster now works with initial_level (#769) @yashlamba

Use joblib to parallelize ensemble fitting for Rocket classifier (#796) @kachayev

Update maintenance tools (#829) @mloning

Undo pmdarima hotfix and avoid pmdarima 1.8.1 (#831) @aaronreidsmith

Hotfix pmdarima version (#828) @aiwalter

Added

Added Guerrero method for lambda estimation to BoxCoxTransformer (#778) (#791) @GuzalBulatova

New forecasting metrics (#801) @RNKuhns

Implementation of DirRec reduction strategy (#779) @luiszugasti

Added cutoff to BaseGridSearch to use any grid search inside evaluate… (#825) @aiwalter

Added pd.DataFrame transformation for Imputer and HampelFilter (#830) @aiwalter

Added default params for some transformers (#834) @aiwalter

Added several docstring examples (#835) @aiwalter

Added skip-inverse-transform tag for Imputer and HampelFilter (#788) @aiwalter

Added a reference to alibi-detect (#815) @satya-pattnaik

All contributors: @GuzalBulatova, @RNKuhns, @aaronreidsmith, @aiwalter, @kachayev, @ltsaprounis, @luiszugasti, @mloning, @satya-pattnaik and @yashlamba
Source code(tar.gz)
Source code(zip)
v0.6.0(Apr 15, 2021)
What's New

Fixed

Fix counting for Github's automatic language discovery (#812) @xuyxu

Fix counting for Github's automatic language discovery (#811) @xuyxu

Fix examples CI checks (#793) @mloning

Fix TimeSeriesForestRegressor (#777) @mloning

Fix Deseasonalizer docstring (#737) @mloning

SettingWithCopyWarning in Prophet with exogenous data (#735) @jschemm

Correct docstrings for check_X and related functions (#701) @Lovkush-A

Fixed bugs mentioned in #694 (#697) @AidenRushbrooke

fix typo in CONTRIBUTING.md (#688) @luiszugasti

Fix duplicacy in the contribution's list (#685) @afzal442

HIVE-COTE 1.0 fix (#678) @MatthewMiddlehurst

Changed

Update sklearn version (#810) @mloning

Remove soft dependency check for numba (#808) @mloning

Modify tests for forecasting reductions (#756) @Lovkush-A

Upgrade nbqa (#794) @MarcoGorelli

Enhanced exception message of splitters (#771) @aiwalter

Enhance forecasting model selection/evaluation (#739) @mloning

Pin PyStan version (#751) @mloning

master to main conversion in docs folder closes #644 (#667) @ayan-biswas0412

Update governance (#686) @mloning

remove MSM from unit tests for now (#698) @TonyBagnall

Make update_params=true by default (#660) @pabworks

update dataset names (#676) @TonyBagnall

Added

Add support for exogenous variables to forecasting reduction (#757) @mloning

Added forecasting docstring examples (#772) @aiwalter

Added the agg argument to EnsembleForecaster (#774) @Ifeanyi30

Added OptionalPassthrough transformer (#762) @aiwalter

Add doctests (#766) @mloning

Multiplexer forecaster (#715) @koralturkk

Upload source tarball to PyPI during releases (#749) @dsherry

Create developer guide (#734) @mloning

Refactor TSF classifier into TSF regressor (#693) @luiszugasti

Outlier detection with HampelFilter (#708) @aiwalter

changes to contributing.md to include directions to installation (#695) @kanand77

Evaluate (example and fix) (#690) @aiwalter

Knn unit tests (#705) @TonyBagnall

Knn transpose fix (#689) @TonyBagnall

Evaluate forecaster function (#657) @aiwalter

Multioutput reduction strategy for forecasting (#659) @Lovkush-A

All contributors: @AidenRushbrooke, @Ifeanyi30, @Lovkush-A, @MarcoGorelli, @MatthewMiddlehurst, @TonyBagnall, @afzal442, @aiwalter, @ayan-biswas0412, @dsherry, @jschemm, @kanand77, @koralturkk, @luiszugasti, @mloning, @pabworks and @xuyxu
Source code(tar.gz)
Source code(zip)
v0.5.3(Feb 6, 2021)
What's New

Fixed

Fix reduced regression forecaster reference (#658) @mloning

Address Bug #640 (#642) @patrickzib

Ed knn (#638) @TonyBagnall

Euclidean distance for KNNs (#636) @goastler

Changed

Pin NumPy 1.19 (#643) @mloning

Update CoC committee (#614) @mloning

Benchmarking issue141 (#492) @ViktorKaz

Catch22 Refactor & Multithreading (#615) @MatthewMiddlehurst

Added

Create new factory method for forecasting via reduction (#635) @Lovkush-A

Feature ForecastingRandomizedSearchCV (#634) @pabworks

Added Imputer for missing values (#637) @aiwalter

Add expanding window splitter (#627) @koralturkk

Forecasting User Guide (#595) @Lovkush-A

Add data processing functionality to convert between data formats (#553) @RNKuhns

Add basic parallel support for ElasticEnsemble (#546) @xuyxu

All contributors: @Lovkush-A, @MatthewMiddlehurst, @RNKuhns, @TonyBagnall, @ViktorKaz, @aiwalter, @goastler, @koralturkk, @mloning, @pabworks, @patrickzib and @xuyxu
Source code(tar.gz)
Source code(zip)
v0.5.2(Jan 13, 2021)
What's New

Fixed

Fix ModuleNotFoundError issue (#613) @Hephaest

Fixes _fit(X) in KNN (#610) @TonyBagnall

UEA TSC module improvements 2 (#599) @TonyBagnall

Fix sktime.classification.frequency_based not found error (#606) @Hephaest

UEA TSC module improvements 1 (#579) @TonyBagnall

Relax numba pinning (#593) @dhirschfeld

Fix fh.to_relative() bug for DatetimeIndex (#582) @aiwalter

All contributors: @Hephaest, @MatthewMiddlehurst, @TonyBagnall, @aiwalter and @dhirschfeld
Source code(tar.gz)
Source code(zip)
v0.5.1(Dec 29, 2020)
What's New

Added

Add ARIMA (#559) @HYang1996

Add fbprophet wrapper (#515) @aiwalter

Add MiniRocket and MiniRocketMultivariate (#542) @angus924

Add Cosine, ACF and PACF transformers (#509) @afzal442

Add example notebook Window Splitters (#555) @juanitorduz

Add SlidingWindowSplitter visualization on doctrings (#554) @juanitorduz

Fixed

Pin pandas version to fix pandas-related AutoETS error on Linux (#581) @mloning

Fixed default argument in docstring in SlidingWindowSplitter (#556) @ngupta23

All contributors: @HYang1996, @TonyBagnall, @afzal442, @aiwalter, @angus924, @juanitorduz, @mloning and @ngupta23
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v0.5.0(Dec 19, 2020)
What's New

Added

Add tests for forecasting with exogenous variables (#547) @mloning

Add HCrystalBall wrapper (#485) @MichalChromcak

Tbats (#527) @aiwalter

Added matrix profile using stumpy (#471) @utsavcoding

User guide (#377) @mloning

Add GitHub workflow for building and testing on macOS (#505) @mloning

[DOC] Add dtaidistance (#502) @mloning

Implement the feature_importances_ property for RISE (#497) @AaronX121

Add scikit-fda to the list of related software (#495) @vnmabus

[DOC] Add roadmap to docs (#467) @mloning

Add parallelization for RandomIntervalSpectralForest (#482) @AaronX121

New Ensemble Forecasting Methods (#333) @magittan

CI run black formatter on notebooks as well as Python scripts (#437) @MarcoGorelli

Implementation of catch22 transformer, CIF classifier and dictionary based clean-up (#453) @MatthewMiddlehurst

Added write dataset to ts file functionality (#438) @whackteachers

Added ability to load from csv containing long-formatted data (#442) @AidenRushbrooke

Transform typing (#420) @mloning

Changed

Refactoring utils and transformer module (#538) @mloning

Update README (#454) @mloning

Clean up example notebooks (#548) @mloning

Update README.rst (#536) @aiwalter

[Doc]Updated load_data.py (#496) @Afzal-Ind

Update forecasting.py (#487) @raishubham1

update basic motion description (#475) @vollmersj

[DOC] Update docs in benchmarking/data.py (#489) @Afzal-Ind

Edit Jupyter Notebook 01_forecasting (#486) @bmurdata

Feature & Performance improvements of SFA/WEASEL (#457) @patrickzib

Moved related software from wiki to docs (#439) @mloning

Fixed

Fixed issue outlined in issue 522 (#537) @ngupta23

Fix plot-series (#533) @gracewgao

added mape_loss and cosmetic fixes to notebooks (removed kernel) (#500) @tch

Fix azure pipelines (#506) @mloning

[DOC] Fix broken docstrings of RandomIntervalSpectralForest (#473) @AaronX121

Add back missing bibtex reference to classifiers (#468) @whackteachers

Avoid seaborn warning (#472) @davidbp

Bump pre-commit versions, run again on notebooks (#469) @MarcoGorelli

Fix series validation (#463) @mloning

Fix soft dependency imports (#446) @mloning

Fix bug in AutoETS (#445) @HYang1996

Add ForecastingHorizon class to docs (#444) @mloning

Removed

Remove manylinux1 (#458) @mloning

All contributors: @AaronX121, @Afzal-Ind, @AidenRushbrooke, @HYang1996, @MarcoGorelli, @MatthewMiddlehurst, @MichalChromcak, @TonyBagnall, @aiwalter, @bmurdata, @davidbp, @gracewgao, @magittan, @mloning, @ngupta23, @patrickzib, @raishubham1, @tch, @utsavcoding, @vnmabus, @vollmersj and @whackteachers
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v0.4.3(Oct 20, 2020)
What's New

Added

Support for 3d numpy array (#405) @mloning

Support for downloading dataset from UCR UEA time series classification data set repository (#430) @Emiliathewolf

Univariate time series regression example to TSFresh notebook (#428) @evanmiller29

Parallelized TimeSeriesForest using joblib. (#408) @kkoziara

Unit test for multi-processing (#414) @kkoziara

Add date-time support for forecasting framework (#392) @mloning

Changed

Performance improvements of dictionary classifiers (#398) @patrickzib

Fixed

Fix links in Readthedocs and Binder launch button (#416) @mloning

Fixed small bug in performance metrics (#422) @krumeto

Resolved warnings in notebook examples (#418) @alwinw

Better error handling for missing soft dependencies (#410) @alwinw

All contributors: @Emiliathewolf, @alwinw, @evanmiller29, @kkoziara, @krumeto, @mloning and @patrickzib
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v0.4.2(Oct 1, 2020)
What's new

Added

ETSModel with auto-fitting capability (#393) @HYang1996

WEASEL classifier (#391) @patrickzib

Full support for exogenous data in forecasting framework (#382) @mloning, (#380) @mloning

Multivariate dataset for US consumption over time (#385) @SebasKoel

Governance document (#324) @mloning, @fkiraly

Fixed

Documentation fixes (#400) @brettkoonce, (#399) @akanz1, (#404) @alwinw

Changed

Move documentation to ReadTheDocs with support for versioned documentation (#395) @mloning

Refactored SFA implementation (additional features and speed improvements) (#389) @patrickzib

Move prediction interval API to base classes in forecasting framework (#387) @big-o

Documentation improvements (#364) @mloning

Update CI and maintenance tools (#394) @mloning

All contributors: @HYang1996, @SebasKoel, @fkiraly, @akanz1, @alwinw, @big-o, @brettkoonce, @mloning, @patrickzib
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v0.4.1(Jul 9, 2020)
What's new:

Added

TemporalDictionaryEnsemble (#292) @MatthewMiddlehurst

ShapeDTW (#287) @Multivin12

Updated sktime artwork (logo) @mloning

Truncation transformer (#315) @ABostrom

Padding transformer (#316) @ABostrom

Example notebook with feature importance graph for time series forest (#319) @HYang1996

ACSF1 data set (#314) @BandaSaiTejaReddy

Data conversion function from 3d numpy array to nested pandas dataframe (#304) @vedazeren

Changed

Replaced gunpoint dataset in tutorials, added OSULeaf dataset (#295) @marielledado

Updated macOS advanced install instructions (#306) (#308) @sophijka

Updated contributing guidelines (#301) @Ayushmaanseth

Fixed

Typos (#293) @Mo-Saif, (#285) @Pangoraw, (#305) @hiqbal2

Manylinux wheel building (#286) @mloning

KNN compatibility with sklearn (#310) @Cheukting

Docstrings for AutoARIMA (#307) @btrtts

All contributors: @Ayushmaanseth, @Mo-Saif, @Pangoraw, @marielledado, @mloning, @sophijka, @Cheukting, @MatthewMiddlehurst, @Multivin12, @ABostrom, @HYang1996, @BandaSaiTejaReddy, @vedazeren, @hiqbal2, @btrtts
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A unified framework for machine learning with time series

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Releases(v0.15.0)

v0.15.0(Dec 22, 2022)

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v0.14.1(Nov 30, 2022)

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v0.14.0(Nov 7, 2022)

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v0.13.4(Sep 27, 2022)

v0.13.3(Sep 26, 2022)

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v0.13.2(Aug 24, 2022)

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v0.13.1(Aug 10, 2022)

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v0.13.0(Jul 14, 2022)

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v.0.12.1(Jun 28, 2022)

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