Nixtla is an open-source time series forecasting library.

Overview

Nixtla

Nixtla is an open-source time series forecasting library.

We are helping data scientists and developers to have access to open source state-of-the-art forecasting pipelines. For that purpose, we built a complete pipeline that can be deployed in the cloud using AWS and consumed via APIs or consumed as a service. If you want to set up your own infrastructure, follow the instructions in the repository (Azure coming soon).

You can use our fully hosted version as a service through our python SDK (autotimeseries). To consume the APIs on our own infrastructure just request tokens by sending an email to [email protected] or opening a GitHub issue. We currently have free resources available for anyone interested.

We built a fully open-source time-series pipeline capable of achieving 1% of the performance in the M5 competition. Our open-source solution has a 25% better accuracy than Amazon Forecast and is 20% more accurate than fbprophet. It also performs 4x faster than Amazon Forecast and is less expensive.

To reproduce the results: Open In Colab or you can read this Medium Post.

At Nixtla we strongly believe in open-source, so we have released all the necessary code to set up your own time-series processing service in the cloud (using AWS, Azure is WIP). This repository uses continuous integration and deployment to deploy the APIs on our infrastructure.

Python SDK Basic Usage

CI python sdk

Install

PyPI

pip install autotimeseries

How to use

Check the following examples for a full pipeline:

Basic usage

import os

from autotimeseries.core import AutoTS

autotimeseries = AutoTS(bucket_name=os.environ['BUCKET_NAME'],
                        api_id=os.environ['API_ID'],
                        api_key=os.environ['API_KEY'],
                        aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
                        aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])

Upload dataset to S3

train_dir = '../data/m5/parquet/train'
# File with target variables
filename_target = autotimeseries.upload_to_s3(f'{train_dir}/target.parquet')
# File with static variables
filename_static = autotimeseries.upload_to_s3(f'{train_dir}/static.parquet')
# File with temporal variables
filename_temporal = autotimeseries.upload_to_s3(f'{train_dir}/temporal.parquet')

Each time series of the uploaded datasets is defined by the column item_id. Meanwhile the time column is defined by timestamp and the target column by demand. We need to pass this arguments to each call.

columns = dict(unique_id_column='item_id',
               ds_column='timestamp',
               y_column='demand')

Send the job to make forecasts

response_forecast = autotimeseries.tsforecast(filename_target=filename_target,
                                              freq='D',
                                              horizon=28,
                                              filename_static=filename_static,
                                              filename_temporal=filename_temporal,
                                              objective='tweedie',
                                              metric='rmse',
                                              n_estimators=170,
                                              **columns)

Download forecasts

autotimeseries.download_from_s3(filename='forecasts_2021-10-12_19-04-32.csv', filename_output='../data/forecasts.csv')

Forecasting Pipeline as a Service

Our forecasting pipeline is modular and built upon simple APIs:

tspreprocess

CI/CD tspreprocess Lambda CI/CD tspreprocess docker image

Time series usually contain missing values. This is the case for sales data where only the events that happened are recorded. In these cases it is convenient to balance the panel, i.e., to include the missing values to correctly determine the value of future sales.

The tspreprocess API allows you to do this quickly and easily. In addition, it allows one-hot encoding of static variables (specific to each time series, such as the product family in case of sales) automatically.

tsfeatures

CI/CD tsfeatures Lambda CI/CD tsfeatures docker image

It is usually good practice to create features of the target variable so that they can be consumed by machine learning models. This API allows users to create features at the time series level (or static features) and also at the temporal level.

The tsfeatures API is based on the tsfeatures library also developed by the Nixtla team (inspired by the R package tsfeatures) and the tsfresh library.

With this API the user can also generate holiday variables. Just enter the country of the special dates or a file with the specific dates and the API will return dummy variables of those dates for each observation in the dataset.

tsforecast

CI/CD tsforecast Lambda CI/CD tsforecast docker image

The tsforecast API is responsible for generating the time series forecasts. It receives as input the target data and can also receive static variables and time variables. At the moment, the API uses the mlforecast library developed by the Nixtla team using LightGBM as a model.

In future iterations, the user will be able to choose different Deep Learning models based on the nixtlats library developed by the Nixtla team.

tsbenchmarks

CI/CD tsbenchmarks Lambda CI/CD tsbenchmarks docker image

The tsbenchmarks API is designed to easily compare the performance of models based on time series competition datasets. In particular, the API offers the possibility to evaluate forecasts of any frequency of the M4 competition and also of the M5 competition.

These APIs, written in Python and can be consumed through an SDK also written in Python. The following diagram summarizes the structure of our pipeline:

Build your own time-series processing service using AWS

Why ?

We want to contribute to open source and help data scientists and developers to achieve great forecasting results without the need to implement complex pipelines.

How?

If you want to use our hosted version send us an email or open a github issue and ask for API Keys.

If you want to deploy Nixtla on your own AWS Cloud you will need:

  • API Gateway (to handle API calls).
  • Lambda (or some computational unit).
  • SageMaker (or some bigger computational unit).
  • ECR (to store Docker images).
  • S3 (for inputs and outputs).

You will end with an architecture that looks like the following diagram

Each call to the API executes a particular Lambda function depending on the endpoint. That particular lambda function instantiates a SageMaker job using a predefined type of instance. Finally, SageMaker reads the input data from S3 and writes the processed data to S3, using a predefined Docker image stored in ECR.

Run the API locally

  1. Create the environment using make init.
  2. Launch the app using make app.

Create AWS resources

Create S3 buckets

For each service:

  1. Create an S3 bucket. The code of each lambda function will be uploaded here.

Create ECR repositorires

For each service:

  1. Create a private repository for each service.

Lambda Function

For each service:

  1. Create a lambda function with Python 3.7 runtime.
  2. Modify the runtime setting and enter main.handler in the handler.
  3. Go to the configuration:
    • Edit the general configuration and add a timeout of 9:59.
    • Add an existing role capable of reading/writing from/to S3 and running Sagemaker services.
  4. Add the following environment variables:
    • PROCESSING_REPOSITORY_URI: ECR URI of the docker image corresponding to the service.
    • ROLE: A role capable of reading/writing from/to S3 and also running Sagemaker services.
    • INSTANCE_COUNT
    • INSTANCE_TYPE

API Gateway

  1. Create a public REST API (Regional).
  2. For each endpoint in api/main.py… add a resource.
  3. For each created method add an ANY method:
    • Select lambda function.
    • Select Use Lambda Proxy Integration.
    • Introduce the name of the lambda function linked to that resource.
    • Once the method is created select Method Request and set API key required to true.
  4. Deploy the API.

Usage plan

  1. Create a usage plan based on your needs.
  2. Add your API stage.

API Keys

  1. Generate API keys as needed.

Deployment

GitHub secrets

  1. Set the following secrets in your repo:
    • AWS_ACCESS_KEY_ID
    • AWS_SECRET_ACCESS_KEY
    • AWS_DEFAULT_REGION
Comments
  • token request

    token request

    from readme:

    To consume the APIs on our own infrastructure just request tokens by sending an email to [email protected] or opening a GitHub issue. We currently have free resources available for anyone interested.

    Hi! I have sent a mail on Monday (with my work mail) for a token request and since I have received no follow up, I though well of trying to ping here (might have ended into spam). Let me know if having a token is still an option.

    And congrats on this great work you are doing in this (and related) repo(s), even without a token I would still make sure to take it for a test drive :)

    opened by pietroppeter 2
  • Bump numpy from 1.20.3 to 1.21.0 in /tsforecast

    Bump numpy from 1.20.3 to 1.21.0 in /tsforecast

    Bumps numpy from 1.20.3 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • new/arima

    new/arima

    • add restmap src
    • add metric replayer
    • update dockerfile
    • feat: add arima exp
    • update m4 evaluation
    • feat: add prophet exp
    • feat: prophet hpo
    • feat: add auto_arima forecast
    • fix: rm arima experiments
    • fix: add auto_arima args api
    • feat: add try sagemaker run
    opened by FedericoGarza 0
  • add local api generation

    add local api generation

    I uploaded a Dockerfile to reproduce locally the api.zip file used by the lambda function. To run it locally, you need to use:

    • make init_api_docker to initialize the Docker image.
    • make create_api_zip route=tsfeatures to create the api.zip file for the tsfeatures service. tsfeatures can be changed for the other services.
    opened by FedericoGarza 0
  • build(deps): bump numpy from 1.20.3 to 1.22.0 in /tsforecast

    build(deps): bump numpy from 1.20.3 to 1.22.0 in /tsforecast

    Bumps numpy from 1.20.3 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • fix cast error and change unit of measure of distance, fix #15

    fix cast error and change unit of measure of distance, fix #15

    issue explanation:

    • distance is initially computed as a timedelta in nanoseconds
    • the fix for end of date range distance[distance > 183] = 365 - distance[distance > 183] fails in the comparison distance > 183 and is actually assuming that distance is in days

    fix explanation:

    • distance is explictly converted to be measure in days

    related change:

    • removed the conversion of distance to be measured in minutes, it will stay measured in days (I do not think this would affect the rest, but I might be mistaken)
    opened by pietroppeter 1
  • make_holidays: incompatibility of date type and date range fix

    make_holidays: incompatibility of date type and date range fix

    When fixing the distance variables for the beginning and end of the date range distance[distance>183] = 365 - distance[distance>183]

    UFuncTypeError: Cannot cast ufunc 'greater' input 0 from dtype('<m8[ns]') to dtype('<m8') with casting rule 'same_kind'

    opened by kdgutier 1
Owner
Nixtla
Open Source Time Series Forecasting
Nixtla
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

Facebook Research 4.1k Dec 29, 2022
Time series forecasting with PyTorch

Our article on Towards Data Science introduces the package and provides background information. Pytorch Forecasting aims to ease state-of-the-art time

Jan Beitner 2.5k Jan 2, 2023
ETNA is an easy-to-use time series forecasting framework.

ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from classic machine learning to SOTA neural networks, models combination methods and smart backtesting. ETNA is designed to make working with time series simple, productive, and fun.

Tinkoff.AI 674 Jan 7, 2023
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 675 Jan 8, 2023
Continuously evaluated, functional, incremental, time-series forecasting

timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s

Peter Cotton 343 Jan 4, 2023
Open source time series library for Python

PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array

Ross Taylor 2k Jan 2, 2023
An open-source library of algorithms to analyse time series in GPU and CPU.

An open-source library of algorithms to analyse time series in GPU and CPU.

Shapelets 216 Dec 30, 2022
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

alkaline-ml 1.3k Dec 22, 2022
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 2.5k Jan 6, 2023
A python library for Bayesian time series modeling

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

Sam 438 Dec 17, 2022
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.7k Jan 4, 2023
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

null 23.3k Dec 31, 2022
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

null 2.3k Jan 5, 2023
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 15.4k Jan 7, 2023
Automatic extraction of relevant features from time series:

tsfresh This repository contains the TSFRESH python package. The abbreviation stands for "Time Series Feature extraction based on scalable hypothesis

Blue Yonder GmbH 7k Jan 6, 2023
A unified framework for machine learning with time series

Welcome to sktime A unified framework for machine learning with time series We provide specialized time series algorithms and scikit-learn compatible

The Alan Turing Institute 6k Jan 6, 2023
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

null 2.3k Dec 29, 2022
Probabilistic time series modeling in Python

GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (

Amazon Web Services - Labs 3.3k Jan 3, 2023
A Python package for time series classification

pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat

Johann Faouzi 1.4k Jan 1, 2023