tsai 0.2.22

Practical Deep Learning for Time Series / Sequential Data library based on fastai & Pytorch

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Title

Description

State-of-the-art Deep Learning library for Time Series and Sequences.

tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation...

tsai is currently under active development by timeseriesAI.

New in tsai:

• tsai is now available in conda.
• 🚀🚀 MINIROCKET a SOTA Time Series Classification model (now available in Pytorch): You can now check MiniRocket's performance in our new tutorial notebook 10_Time_Series_Classification_and_Regression_with_MiniRocket.ipynb

"Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in less than 10 minutes." A. Dempster et al. (Dec 2020)

• Multi-class and multi-label time series classification notebook: you can also check our new tutorial notebook: 01a_MultiClass_MultiLabel_TSClassification.ipynb

• Self-supervised learning: If you are interested in applying self-supervised learning to time series, you may check our new tutorial notebook: 08_Self_Supervised_MVP.ipynb

• New visualization: We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task for example:

Installation

You can install the latest stable version from pip using:

pip install tsai

Or you can install the cutting edge version of this library from github by doing:

pip install -Uqq git+https://github.com/timeseriesAI/tsai.git

Once the install is complete, you should restart your runtime and then run:

from tsai.all import *

Documentation

Here's the link to the documentation.

How to start using tsai?

To get to know the tsai package, we'd suggest you start with this notebook in Google Colab: 01_Intro_to_Time_Series_Classification It provides an overview of a time series classification task.

We have also develop many other tutorial notebooks.

To use tsai in your own notebooks, the only thing you need to do after you have installed the package is to run this:

from tsai.all import *

Examples

Binary, univariate classification

from tsai.all import *
X, y, splits = get_classification_data('ECG200', split_data=False)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize()
dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)
learn = ts_learner(dls, InceptionTimePlus, metrics=accuracy, cbs=ShowGraph())
learn.fit_one_cycle(100, 3e-4)

Multi-class, multivariate classification

from tsai.all import *
X, y, splits = get_classification_data('LSST', split_data=False)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize(by_sample=True)
dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)
learn = ts_learner(dls, InceptionTimePlus, metrics=accuracy, cbs=ShowGraph())
learn.fit_one_cycle(10, 1e-2)

Multivariate Regression

from tsai.all import *
from sklearn.metrics import mean_squared_error
X_train, y_train, X_test, y_test = get_regression_data('AppliancesEnergy')
rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)
reg = MiniRocketRegressor(scoring=rmse_scorer)
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
mean_squared_error(y_test, y_pred, squared=False)

Univariate Forecasting

from tsai.all import *
ts = get_forecasting_time_series("Sunspots").values
X, y = SlidingWindow(60, horizon=1)(ts)
splits = TimeSplitter(235)(y) 
batch_tfms = TSStandardize()
learn = TSForecaster(X, y, splits=splits, batch_tfms=batch_tfms, bs=512, arch=TST, metrics=mae, cbs=ShowGraph())
learn.fit_one_cycle(50, 1e-3)

How to contribute to tsai?

We welcome contributions of all kinds. Development of features, bug fixes, and other improvements.

We have created a guide to help you start contributing to tsai. You can read it here.

Citing tsai

If you use tsai in your research please use the following BibTeX entry:

@Misc{tsai,
    author =       {Ignacio Oguiza},
    title =        {tsai - A state-of-the-art deep learning library for time series and sequential data},
    howpublished = {Github},
    year =         {2020},
    url =          {https://github.com/timeseriesAI/tsai}
}