![]() ![]() get_X_preds ( X ], y ]) Multi-class, multivariate classification Inference: from tsai.inference import load_learner clf = load_learner ( "models/clf.pkl" ) probas, target, preds = clf. Training: from tsai.basics import * X, y, splits = get_classification_data ( 'ECG200', split_data = False ) tfms = batch_tfms = TSStandardize () clf = TSClassifier ( X, y, splits = splits, path = 'models', arch = "InceptionTimePlus", tfms = tfms, batch_tfms = batch_tfms, metrics = accuracy, cbs = ShowGraph ()) clf. These are just a few examples of how you can use tsai: Binary, univariate classification You have installed the package is to run this: from tsai.all import * Examples To use tsai in your own notebooks, the only thing you need to do after It provides an overview of a time series classification task. To get to know the tsai package, we’d suggest you start with this Plus other custom models like: TransformerModel, LSTMAttention, Temporal Convolutional Network (Bai, 2018)Īn Explainable Convolutional Neural Network (Fauvel, 2021) Multilevel wavelet decomposition network (Wang, 2018) Here’s a list with some of the state-of-the-art models available in With mamba the install process will be much faster and more reliable): conda install - c timeseriesai tsai Documentation You can also install tsai using conda (note that if you replace conda With all its dependencies you can do it by running: pip install tsai Conda install You require any of the dependencies that is not installed, tsai will ask Will not be installed by default (this is the recommended approach. Other soft dependencies (which are only required for selected tasks) Note: starting with tsai 0.3.0 tsai will only install hard dependencies. com / timeseriesAI / tsai pip install - e "tsai" First install PyTorch, and then: git clone https : // github. If you plan to develop tsai yourself, or want to be on the cutting edge, You can install the latest stable version from pip using: pip install tsai New functionality: sklearn-type pipeline transforms, walk-fowardĬross validation, reduced RAM requirements, and a lot of newįunctionality to perform more accurate time series forecasts.Tutorials on data preparation and forecasting. 30 multivariate classification datasetsīased on some of your requests, we are planning to release additional.New datasets: we have increased the number of datasets you can.(RNNAttention, LSTMAttention, GRUAttention), TabFusionTransformer, … New models: PatchTST (Accepted by ICLR 2023), RNN with Attention.What’s new:ĭuring the last few releases, here are some of the most significant ![]() Tsai is currently under active development by timeseriesAI. Tsai is an open-source deep learning package built on top of Pytorch &įastai focused on state-of-the-art techniques for time series tasks likeĬlassification, regression, forecasting, imputation… State-of-the-art Deep Learning library for Time Series and Sequences. ![]()
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