Welcome to PaddleTS

PaddleTS is an easy-to-use Python library for deep time series modeling, focusing on the state-of-the-art deep neural network models based on PaddlePaddle deep learning framework. It aims to provide great flexibility and excellent user experiences for practitioners and professionals. It’s featured with:

A unified data structure named TSDataset for representing time series data with one or multiple target variables and optional different kinds of covariates (e.g. known covariates, observed covariates, static covariates, etc.)
A base model class named PaddleBaseModelImpl , which inherits from the PaddleBaseModel and further encapsulates some routine procedures (e.g. data loading, callbacks setup, loss computation, training loop control, etc.) and allows developers to focus on the implementation of network architectures when developing new models
A set of state-of-the-art deep learning models containing NBEATS, NHiTS, LSTNet, TCN, Transformer, DeepAR(Probabilistic), Informer, etc. for forecasting, TS2Vec for representation
A set of transformation operators for data preprocessing (e.g. missing values/outliers handling, one-hot encoding, normalization, and automatic date/time-related covariates generation, etc.)
A set of analysis operators for quick data exploration (e.g. basic statistics and summary)
Automatic time series modeling module (AutoTS) which supports mainstream Hyper Parameter Optimization algorithms and shows significant improvement on multiple models and datasets
Third-party (e.g. scikit-learn) ML models & data transformations integration