tsai

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

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tsai


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.

What’s new:

During the last few releases, here are some of the most significant
additions to tsai:

  • New models: PatchTST (Accepted by ICLR 2023), RNN with Attention
    (RNNAttention, LSTMAttention, GRUAttention), TabFusionTransformer, …
  • New datasets: we have increased the number of datasets you can
    download using tsai:
    • 128 univariate classification datasets
    • 30 multivariate classification datasets
    • 15 regression datasets
    • 62 forecasting datasets
    • 9 long term forecasting datasets
  • New tutorials:
    PatchTST.
    Based on some of your requests, we are planning to release additional
    tutorials on data preparation and forecasting.
  • New functionality: sklearn-type pipeline transforms, walk-foward
    cross validation, reduced RAM requirements, and a lot of new
    functionality to perform more accurate time series forecasts.
  • Pytorch 2.0 support.

Installation

Installation

Requires Python 3.10 or newer. As of tsai 1.0.0, support for
Python 3.9 has been dropped. See the
CHANGELOG
for full upgrade notes if you’re coming from 0.x.

Pip install

Install the latest stable version from PyPI:

python
pip install tsai

For an editable install (development or bleeding-edge):

python
git clone https://github.com/timeseriesAI/tsai
pip install -e "tsai[dev]"

tsai installs only hard dependencies by default. Optional dependencies
(sktime, tsfresh, PyWavelets, nbformat) are only needed for selected
tasks; tsai will prompt you to install them when required. To install
everything up front:

python
pip install tsai[extras]

Conda install

As of tsai 1.0.0 the conda channel is no longer updated; tsai is
distributed via PyPI only. Older releases remain available on the
timeseriesai conda channel,
but for new installs please use pip.

Documentation

Here’s the link to the
documentation.

Available models:

Here’s a list with some of the state-of-the-art models available in
tsai:

plus other custom models like: TransformerModel, LSTMAttention,
GRUAttention, …

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:

python
from tsai.all import *

Examples

These are just a few examples of how you can use tsai:

Binary, univariate classification

Training:

python
from tsai.basics import *

X, y, splits = get_classification_data('ECG200', split_data=False)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize()
clf = TSClassifier(X, y, splits=splits, path='models', arch="InceptionTimePlus", tfms=tfms, batch_tfms=batch_tfms, metrics=accuracy, cbs=ShowGraph())
clf.fit_one_cycle(100, 3e-4)
clf.export("clf.pkl")

Inference:

python
from tsai.inference import load_learner

clf = load_learner("models/clf.pkl")
probas, target, preds = clf.get_X_preds(X[splits[1]], y[splits[1]])

Multi-class, multivariate classification

Training:

python
from tsai.basics import *

X, y, splits = get_classification_data('LSST', split_data=False)
tfms = [None, TSClassification()]
batch_tfms = TSStandardize(by_sample=True)
mv_clf = TSClassifier(X, y, splits=splits, path='models', arch="InceptionTimePlus", tfms=tfms, batch_tfms=batch_tfms, metrics=accuracy, cbs=ShowGraph())
mv_clf.fit_one_cycle(10, 1e-2)
mv_clf.export("mv_clf.pkl")

Inference:

python
from tsai.inference import load_learner

mv_clf = load_learner("models/mv_clf.pkl")
probas, target, preds = mv_clf.get_X_preds(X[splits[1]], y[splits[1]])

Multivariate Regression

Training:

python
from tsai.basics import *

X, y, splits = get_regression_data('AppliancesEnergy', split_data=False)
tfms = [None, TSRegression()]
batch_tfms = TSStandardize(by_sample=True)
reg = TSRegressor(X, y, splits=splits, path='models', arch="TSTPlus", tfms=tfms, batch_tfms=batch_tfms, metrics=rmse, cbs=ShowGraph(), verbose=True)
reg.fit_one_cycle(100, 3e-4)
reg.export("reg.pkl")

Inference:

python
from tsai.inference import load_learner

reg = load_learner("models/reg.pkl")
raw_preds, target, preds = reg.get_X_preds(X[splits[1]], y[splits[1]])

The ROCKETs (RocketClassifier, RocketRegressor, MiniRocketClassifier,
MiniRocketRegressor, MiniRocketVotingClassifier or
MiniRocketVotingRegressor) are somewhat different models. They are not
actually deep learning models (although they use convolutions) and are
used in a different way.

⚠️ You’ll also need to install sktime to be able to use them. You can
install it separately:

python
pip install sktime

or use:

python
pip install tsai[extras]

Training:

python
from sklearn.metrics import mean_squared_error, make_scorer
from tsai.data.external import get_Monash_regression_data
from tsai.models.MINIROCKET import MiniRocketRegressor

X_train, y_train, *_ = get_Monash_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)
reg.save('MiniRocketRegressor')

Inference:

python
from sklearn.metrics import mean_squared_error
from tsai.data.external import get_Monash_regression_data
from tsai.models.MINIROCKET import load_minirocket

*_, X_test, y_test = get_Monash_regression_data('AppliancesEnergy')
reg = load_minirocket('MiniRocketRegressor')
y_pred = reg.predict(X_test)
mean_squared_error(y_test, y_pred, squared=False)

Forecasting

You can use tsai for forecast in the following scenarios:

  • univariate or multivariate time series input
  • univariate or multivariate time series output
  • single or multi-step ahead

You’ll need to: * prepare X (time series input) and the target y (see
documentation)
* select PatchTST or one of tsai’s models ending in Plus (TSTPlus,
InceptionTimePlus, TSiTPlus, etc). The model will auto-configure a head
to yield an output with the same shape as the target input y.

Single step

Training:

python
from tsai.basics import *

ts = get_forecasting_time_series("Sunspots").values
X, y = SlidingWindow(60, horizon=1)(ts)
splits = TimeSplitter(235)(y) 
tfms = [None, TSForecasting()]
batch_tfms = TSStandardize()
fcst = TSForecaster(X, y, splits=splits, path='models', tfms=tfms, batch_tfms=batch_tfms, bs=512, arch="TSTPlus", metrics=mae, cbs=ShowGraph())
fcst.fit_one_cycle(50, 1e-3)
fcst.export("fcst.pkl")

Inference:

python
from tsai.inference import load_learner

fcst = load_learner("models/fcst.pkl", cpu=False)
raw_preds, target, preds = fcst.get_X_preds(X[splits[1]], y[splits[1]])
raw_preds.shape
# torch.Size([235, 1])

Multi-step

This example show how to build a 3-step ahead univariate forecast.

Training:

python
from tsai.basics import *

ts = get_forecasting_time_series("Sunspots").values
X, y = SlidingWindow(60, horizon=3)(ts)
splits = TimeSplitter(235, fcst_horizon=3)(y) 
tfms = [None, TSForecasting()]
batch_tfms = TSStandardize()
fcst = TSForecaster(X, y, splits=splits, path='models', tfms=tfms, batch_tfms=batch_tfms, bs=512, arch="TSTPlus", metrics=mae, cbs=ShowGraph())
fcst.fit_one_cycle(50, 1e-3)
fcst.export("fcst.pkl")

Inference:

python
from tsai.inference import load_learner
fcst = load_learner("models/fcst.pkl", cpu=False)
raw_preds, target, preds = fcst.get_X_preds(X[splits[1]], y[splits[1]])
raw_preds.shape
# torch.Size([235, 3])

Input data format

The input format for all time series models and image models in tsai is
the same. An np.ndarray (or array-like object like zarr, etc) with 3
dimensions:

[# samples x # variables x sequence length]

The input format for tabular models in tsai (like TabModel,
TabTransformer and TabFusionTransformer) is a pandas dataframe. See
example.

How to contribute to tsai?

We welcome contributions of all kinds. Development of enhancements, bug
fixes, documentation, tutorial notebooks, …

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

Enterprise support and consulting services:

Want to make the most out of timeseriesAI/tsai in a professional
setting? Let us help. Send us an email to learn more:
[email protected]

Citing tsai

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

text
@Misc{tsai,
    author =       {Ignacio Oguiza},
    title =        {tsai - A state-of-the-art deep learning library for time series and sequential data},
    howpublished = {Github},
    year =         {2023},
    url =          {https://github.com/timeseriesAI/tsai}
}

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