Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Overview

Description

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc.

Kats is released by Facebook's Infrastructure Data Science team. It is available for download on PyPI.

Important links

Installation in Python

Kats is on PyPI, so you can use pip to install it.

pip install --upgrade pip
pip install kats

Examples

Here are a few sample snippets from a subset of Kats offerings:

Forecasting

Using Prophet model to forecast the air_passengers data set.

from kats.consts import TimeSeriesData
from kats.models.prophet import ProphetModel, ProphetParams

# take `air_passengers` data as an example
air_passengers_df = pd.read_csv("../kats/data/air_passengers.csv")

# convert to TimeSeriesData object
air_passengers_ts = TimeSeriesData(air_passengers_df)

# create a model param instance
params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results

# create a prophet model instance
m = ProphetModel(air_passengers_ts, params)

# fit model simply by calling m.fit()
m.fit()

# make prediction for next 30 month
fcst = m.predict(steps=30, freq="MS")

Detection

Using CUSUM detection algorithm on simulated data set.

# import packages
from kats.consts import TimeSeriesData
from kats.detectors.cusum_detection import CUSUMDetector

# simulate time series with increase
np.random.seed(10)
df_increase = pd.DataFrame(
    {
        'time': pd.date_range('2019-01-01', '2019-03-01'),
        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),
    }
)

# convert to TimeSeriesData object
timeseries = TimeSeriesData(df_increase)

# run detector and find change points
change_points = CUSUMDetector(timeseries).detector()

TSFeatures

We can extract meaningful features from the given time series data

# Initiate feature extraction class
from kats.tsfeatures.tsfeatures import TsFeatures

# take `air_passengers` data as an example
air_passengers_df = pd.read_csv("../kats/data/air_passengers.csv")

# convert to TimeSeriesData object
air_passengers_ts = TimeSeriesData(air_passengers_df)

# calculate the TsFeatures
features = TsFeatures().transform(air_passengers_ts)

Changelog

Version 0.1.0

  • Initial release

License

Kats is licensed under the MIT license.

Comments
  • ImportError

    ImportError

    Hello!

    When I was trying to import some kats functionalities on my Jupyter Notebook, it came back with a error message as below:

    ImportError Traceback (most recent call last) in ----> 1 from kats.detectors.outlier import OutlierDetector

    ~\Anaconda3\lib\site-packages\kats_init_.py in 1 from . import consts # noqa ----> 2 from . import utils # noqa 3 from . import detectors # noqa 4 from . import models # noqa 5 from . import tsfeatures # noqa

    ImportError: cannot import name 'utils' from partially initialized module 'kats' (most likely due to a circular import) (C:\Users\49683\Anaconda3\lib\site-packages\kats_init_.py)

    Anyone knows how to resolve this issue? Thanks in advance!

    opened by davidguo-7 15
  • Add minimal installation option using MINIMAL_KATS

    Add minimal installation option using MINIMAL_KATS

    As commented on https://github.com/facebookresearch/Kats/issues/101#issuecomment-947126195, having an environment variable that's exclusive for kats avoids installation issues on projects that have multiple other dependencies, and install them using commands like pip install -r requirements.txt.

    In that scenario, setting MINIMAL_KATS avoids unintended side effects on any other dependency that understands the MINIMAL environment variable.

    CLA Signed Merged 
    opened by adamantike 10
  • Light Install on Windows

    Light Install on Windows

    I am interested in using the detectors in kats for my project (on windows), but the current install is too heavy (requiring Prophet and PyTorch and other heavy libraries). Is there a light version install for kats that provides the bare bone utility functions. If not, could one be made available?

    I see that setup.py has some option like this, but how do I enable this while installing from pip on windows?

    Thanks!

    opened by ngupta23 10
  • unable to build wheel for fbprophet while trying to install Kats in Win 10

    unable to build wheel for fbprophet while trying to install Kats in Win 10

    1. My environment is Win 10, with conda 4.9.2 and Python 3.7.6
    2. While trying to install Kats (pip install kats), I am getting the error: Building wheel for fbprophet (setup.py) ... error
    3. More lines from the error message are in attached file Kats_error.txt
    4. I had an older working fbprophet, faced same errors in trying to upgrade (pip install --upgrade fbprophet).
    5. Anybody know how to fix this?
    opened by kunalrayind 10
  • Installation breaks on Windows10 (conda environment)

    Installation breaks on Windows10 (conda environment)

    Hi everyone,

    When I try to install kats on my machine (Windows 10, build see attached screenshot), the installation breaks building the wheel for fbprophet.

    I am trying this on a fresh conda environment (conda version 4.10.1) and python3.8. My only guess is that this has todo with the ability to install pystan on windows, which is not possible without access to WSL2 (which I do not have because of corporate restrictions).

    I will attach the error message below.

    Attachments

    Windows version

    image

    Error message

    Error Message (click)

    
    ERROR: Command errored out with exit status 1:
       command: 'C:\Users\matheiss\Miniconda3\envs\tiresias-kats\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"'; __file__='"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\matheiss\AppData\Local\Temp\1\pip-wheel-xzjkrfhe'
           cwd: C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\
      Complete output (42 lines):
      running bdist_wheel
      running build
      running build_py
      creating build
      creating build\lib
      creating build\lib\fbprophet
      creating build\lib\fbprophet\stan_model
      Traceback (most recent call last):
        File "<string>", line 1, in <module>
        File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 122, in <module>
          setup(
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\setuptools\__init__.py", line 163, in setup
          return distutils.core.setup(**attrs)
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\core.py", line 148, in setup
          dist.run_commands()
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 966, in run_commands
          self.run_command(cmd)
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
          cmd_obj.run()
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\wheel\bdist_wheel.py", line 299, in run
          self.run_command('build')
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\cmd.py", line 313, in run_command
          self.distribution.run_command(command)
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
          cmd_obj.run()
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\command\build.py", line 135, in run
          self.run_command(cmd_name)
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\cmd.py", line 313, in run_command
          self.distribution.run_command(command)
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
          cmd_obj.run()
        File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 48, in run
          build_models(target_dir)
        File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 38, in build_models
          StanBackendEnum.get_backend_class(backend).build_model(target_dir, MODEL_DIR)
        File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\fbprophet\models.py", line 209, in build_model
          import pystan
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\pystan\__init__.py", line 9, in <module>
          from pystan.api import stanc, stan
        File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\pystan\api.py", line 13, in <module>
          import pystan._api  # stanc wrapper
      ImportError: DLL load failed while importing _api: Das angegebene Modul wurde nicht gefunden.
      ----------------------------------------
      ERROR: Failed building wheel for fbprophet
      Running setup.py clean for fbprophet
    Failed to build fbprophet
    Installing collected packages: fbprophet, ax-platform, attrs, kats
        Running setup.py install for fbprophet ... error
        ERROR: Command errored out with exit status 1:
         command: 'C:\Users\matheiss\Miniconda3\envs\tiresias-kats\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"'; __file__='"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\matheiss\AppData\Local\Temp\1\pip-record-nj09haiu\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\matheiss\Miniconda3\envs\tiresias-kats\Include\fbprophet'
             cwd: C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\
        Complete output (44 lines):
        running install
        running build
        running build_py
        creating build
        creating build\lib
        creating build\lib\fbprophet
        creating build\lib\fbprophet\stan_model
        Traceback (most recent call last):
          File "<string>", line 1, in <module>
          File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 122, in <module>
            setup(
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\setuptools\__init__.py", line 163, in setup
            return distutils.core.setup(**attrs)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\core.py", line 148, in setup
            dist.run_commands()
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 966, in run_commands
            self.run_command(cmd)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
            cmd_obj.run()
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\setuptools\command\install.py", line 61, in run
            return orig.install.run(self)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\command\install.py", line 545, in run
            self.run_command('build')
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\cmd.py", line 313, in run_command
            self.distribution.run_command(command)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
            cmd_obj.run()
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\command\build.py", line 135, in run
            self.run_command(cmd_name)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\cmd.py", line 313, in run_command
            self.distribution.run_command(command)
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\distutils\dist.py", line 985, in run_command
            cmd_obj.run()
          File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 48, in run
            build_models(target_dir)
          File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\setup.py", line 38, in build_models
            StanBackendEnum.get_backend_class(backend).build_model(target_dir, MODEL_DIR)
          File "C:\Users\matheiss\AppData\Local\Temp\1\pip-install-5dy9pwq7\fbprophet_6fb4ff087aaf40478b71089d7e634e82\fbprophet\models.py", line 209, in build_model
            import pystan
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\pystan\__init__.py", line 9, in <module>
            from pystan.api import stanc, stan
          File "C:\Users\matheiss\Miniconda3\envs\tiresias-kats\lib\site-packages\pystan\api.py", line 13, in <module>
            import pystan._api  # stanc wrapper
        ImportError: DLL load failed while importing _api: Das angegebene Modul wurde nicht gefunden.
        ----------------------------------------
    ERROR: Command errored out with exit status 1: 'C:\Users\matheiss\Miniconda3\envs\tiresias-kats\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"'; __file__='"'"'C:\\Users\\matheiss\\AppData\\Local\\Temp\\1\\pip-install-5dy9pwq7\\fbprophet_6fb4ff087aaf40478b71089d7e634e82\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\matheiss\AppData\Local\Temp\1\pip-record-nj09haiu\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\matheiss\Miniconda3\envs\tiresias-kats\Include\fbprophet' Check the logs for full command output.
    

    opened by mjt91 10
  • How to get exact outliers in Univariate Time Series using OutlierDetector?

    How to get exact outliers in Univariate Time Series using OutlierDetector?

    Hello, I'm trying to analyze the Outlier Detection framework for my project but it appears like the model returns the outlier range (not the exact index). Below are the details about my dataset.

    2019-01-01 | 35 2019-01-02 | 32 2019-01-03 | 30 2019-01-04 | 31 2019-01-05 | 44 2019-01-06 | 29 2019-01-07 | 45 2019-01-08 | 43 2019-01-09 | 500 2019-01-10 | 27 2019-01-11 | 38 ..... I would expect the model to return the outlier as "500" and date as "2019-01-09". But the model returns as below. ts_outDetection.outliers[0] -> [Timestamp('2019-01-06 00:00:00'), Timestamp('2019-01-07 00:00:00'), Timestamp('2019-01-08 00:00:00'), Timestamp('2019-01-09 00:00:00'), Timestamp('2019-01-10 00:00:00'), Timestamp('2019-01-11 00:00:00'), Timestamp('2019-01-12 00:00:00')]

    Can someone help me to understand the outlier detector concept in Kats or direct me to the reference document(if any) please? Let me know if you need more details. FB Kats Issue

    opened by sthirumoorthi 9
  • Use running index as time_col

    Use running index as time_col

    I have time series data with the time_col is the index of the dataframe:

    df = [index  value 
                0         31
                1          22
                2         15
                3         77]
    

    When I am trying to convert it to TimeSeriesData, the index is automatically transform to epoch time. (e.g. 1970-01-01 00:00:00.000000095) Is there a way to keep the time_col as the mere index when using TimeSeriesData

    opened by orko19 9
  • AttributeError: 'pandas._libs.properties.CachedProperty' object has no attribute 'func'

    AttributeError: 'pandas._libs.properties.CachedProperty' object has no attribute 'func'

    Hi there,

    I face an error when I run m.fit() on the "Forecasting with Ensemble model" section. image

    Could please anyone share how to address it?

    Many thank

    opened by dreldrel 8
  • updating naming of statsmodels ARIMA

    updating naming of statsmodels ARIMA

    Fixes

    NotImplementedError: 
    statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have
    been removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .
    between arima and model) and statsmodels.tsa.SARIMAX.
    
    statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and
    is both well tested and maintained. It also offers alternative specialized
    parameter estimators
    
    CLA Signed 
    opened by ourownstory 8
  • CUMSUMDetector example doesn't work

    CUMSUMDetector example doesn't work

    Hi,

    Thank you very much for sharing your work! I try to learn from Kats 202 - Detection with Kats and use it on my data, but there is an issue. I don't manage to see what happens when I run cell 2(generating df_increase_decrease) and 3 (using CUMSUMDetector function) from Kats 202 - Detection with Kats. Instead of having the graph that is visible in the document at the output 3, I get this ERROR: "view limit minimum -7.75699... is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units".

    I don't know what causes this issue, I have had a look around on different forums and I can't find the solution. I would be very happy if you could help me.

    Regards, Adriana

    opened by adalaza12 8
  • Upgrade to Prophet 1.1

    Upgrade to Prophet 1.1

    Hello! We recently released v1.1 of Prophet: https://github.com/facebook/prophet/releases/tag/v1.1 which removes the pystan==2.19.1.1 dependency and has binary distributions (python 3.7-3.10) uploaded to PyPI. I think specifying prophet >= 1.1 will make the installation process for Kats a lot smoother.

    opened by tcuongd 6
  • not able to import GetMetaData

    not able to import GetMetaData

    Hi Everyone,

    I am not able to import from kats.models.metalearner.get_metadata import GetMetaData, getting below error

    ImportError: cannot import name '_mul_broadcast_shape' from 'gpytorch.utils.broadcasting' (/opt/conda/lib/python3.9/site-packages/gpytorch/utils/broadcasting.py)

    Can anyone suggest me how to resolve this issue? I tried to optimize https://github.com/facebookresearch/Kats/blob/main/kats/models/metalearner/get_metadata.py file but getting same issue.

    Regards, Ravi

    opened by rvipandey 0
  • Statsmodel version issue

    Statsmodel version issue

    Hi, I ran into multiple issues while running the KatsEnsemble function. I read in closed issues that statsmodels version ==0.12.2 currently supports the Kats package. Although, when I tried to downgrade the statsmodels version from 0.13.2 I'm getting below error. Screen Shot 2022-09-21 at 2 42 22 PM

    opened by madhuri1991 1
  • What parameters can be used for specify change directions in bayesian and robust changepoint detection models?

    What parameters can be used for specify change directions in bayesian and robust changepoint detection models?

    Hi there! In the detection tutorial notebook it is shown for the CUSUMDetector model a parameter, change_directions, for specifying what type of change to be detected. What is the analogous parameter in the case of BOCPDetector, and RobustStatDetector? Or in which other form I can specify an increase or decrease? A working example could be the same as the tutorial.

    opened by jscanass 0
  • AttributeError: 'StanModel' object has no attribute 'fit_class'

    AttributeError: 'StanModel' object has no attribute 'fit_class'

    File "/opt/miniconda3/lib/python3.9/site-packages/kats/models/prophet.py", line 285, in fit self.model = prophet.fit(df=df) File "/opt/miniconda3/lib/python3.9/site-packages/fbprophet/forecaster.py", line 1166, in fit self.params = self.stan_backend.fit(stan_init, dat, **kwargs) File "/opt/miniconda3/lib/python3.9/site-packages/fbprophet/models.py", line 245, in fit self.stan_fit = self.model.optimizing(**args) File "/opt/miniconda3/lib/python3.9/site-packages/pystan/model.py", line 542, in optimizing fit = self.fit_class(data, seed) AttributeError: 'StanModel' object has no attribute 'fit_class'

    opened by nodream007 1
  • GMModel Tutorial Bug

    GMModel Tutorial Bug

    In the Global Model example section 2.2 (https://github.com/facebookresearch/Kats/blob/main/tutorials/kats_205_globalmodel.ipynb), the test_TSs was generated in the same way as train_TSs with the same start time. Is this a typo? Wouldn't there be data leakage since test_TSs is essentially a subset of train_TSs?

    Also why does GMModel take a list of TimeSeriesData? If we have one timeseries, are we supposed to create a list of TimeSeriesData via the expanding window method?

    opened by ericho-bbai 0
  • Enhancement: Can you kindly support the tutorial notebook of the metalearning anomaly detection?

    Enhancement: Can you kindly support the tutorial notebook of the metalearning anomaly detection?

    Recently I am reading the paper MOSPAT https://arxiv.org/pdf/2205.11755.pdf. It's an awesome work and as the paper describes, the code is published in this repo. While I can only found the related code in the detectors. And I cannot reproduce the paper's results. So can anyone kindly support the doc or the anything resources to reproduce the paper?

    Thanks a lot. @rohanfb

    opened by oliveshell 0
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The Alan Turing Institute 5.7k Sep 27, 2022
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. I

Salesforce 2.7k Sep 27, 2022
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 623 Sep 27, 2022
A Python implementation of GRAIL, a generic framework to learn compact time series representations.

GRAIL A Python implementation of GRAIL, a generic framework to learn compact time series representations. Requirements Python 3.6+ numpy scipy tslearn

null 3 Nov 24, 2021
STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

STUMPY STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of tim

TD Ameritrade 2.4k Sep 29, 2022
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

AutoViz and Auto_ViML 486 Sep 30, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Sep 21, 2022
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Prophet: Automatic Forecasting Procedure Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends ar

Facebook 15k Oct 1, 2022