简体中文 | English | 日本語

PaddleTS - PaddlePaddle-based Time Series Modeling in Python

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, Informer, etc. for forecasting, TS2Vec, CoST, etc. for representation, AutoEncoder, VAE, AnomalyTransformer, etc. for anomaly detection
• 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, pyod) ML models & data transformations integration
• Time series model ensemble

最新动态

• [2023-09-01] :fire::fire: PaddleX releases version 2.0， provides a one-stop full-process high-efficiency development platform for flying paddle ecological model training, pressure, and push. Its mission is to help AI technology quickly land, and its vision is to make everyone an AI Developer!
• PaddleTS now supports time series classification
• PaddleTS releases 6 new time series models. USAD(UnSupervised Anomaly Detection) and MTAD-GAT(Multivariate Time-series Anomaly Detection via Graph Attention Network) for anomaly detection, CNN and Inception Time for time series classification, SCINet(Sample Convolution and Interaction Network) and TFT(Temporal Fusion Transformer) for forecasting
• Paddle Inference is now available for PaddleTS time series forecasting and anomaly detection
• PaddleTS now supports both model-agnostic and model-specific explanation
• PaddleTS now supports representation-based time series cluster and classification

Please also see release notes to get exhaustive update lists.

In the future, more advanced features will be coming, including:

• More time series models
• Scenario-specific pipelines which aim to provide an end-to-end solution for solving real-world business problems
• And more

Technical exchange and cooperation

PaddleX provides a one-stop development platform for selected models of PaddlePaddle, which can complete the full process development of data, training, evaluation and reasoning of common AI tasks through four buttons.

• PaddleX currently covers areas such as image classification, object detection, image segmentation, 3D, OCR, and time series prediction, and has built-in 36 basic single models, such as RP-DETR, PP-YOLOE, PP-HGNet, PP-LCNet, PP- LiteSeg, PP-TS, TimesNet, PatchTST, etc.; integrated 12 practical industrial solutions, such as PP-OCRv4, PP-ChatOCR, PP-ShiTu, PP-TS, vehicle-mounted road waste detection, identification of prohibited wildlife products, etc.
• PaddleX provides two AI development modes: "Toolbox" and "Developer". The toolbox mode can tune key hyperparameters without code, and the developer mode can perform single-model training, push and multi-model serial inference with low code, and supports both cloud and local terminals.
• PaddleX also supports joint innovation and development, profit sharing! At present, PaddleX is rapidly iterating, and welcomes the participation of individual developers and enterprise developers to create a prosperous AI technology ecosystem!

Scan the QR code below on WeChat to add operation students, and reply [paddlex], operation students will invite you to join the official communication group for more efficient questions and answers.

Welcome to the AI Studio PaddleX Community Channel for further communication. Address Direct：https://aistudio.baidu.com/community/channel/610

More About PaddleTS

Specifically, PaddleTS consists of the following modules:

Installation

Prerequisites

• python >= 3.7
• paddlepaddle >= 2.3

Install paddlets via pip:

pip install paddlets

To get more detailed information, please refer to Installation.

Documentation

• Get Started

• API Reference

Community

Welcome to the AI Studio PaddleX Community Channel for further communication. Address Direct：https://aistudio.baidu.com/community/channel/610

Contributions

We appreciate all kinds of contributions. Please let us know if you encounter any bug by filing an issue.

If you are willing to contribute back bug-fixes, please go ahead without any further discussion.

If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss with us. Sending a PR without discussion might end up with rejection because we might be taking the core in a different direction than you might be aware of.

License

PaddleTS has an Apache-style license, as found in the LICENSE file.