A python library for user-friendly forecasting and anomaly detection on time series.

Unofficial implementation of iTransformer - SOTA Time Series Forecasting using Attention networks, out of Tsinghua / Ant group

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

Scalable and user friendly neural 🧠 forecasting algorithms.

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learna…

Sequence modeling benchmarks and temporal convolutional networks

Deep learning PyTorch library for time series forecasting

Time series forecasting especially in LSTF compare，include Informer, Autoformer, Reformer, Pyraformer, FEDformer, Transformer, MTGNN, LSTNet, Graph WaveNet

手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

Implementation of Convolutional LSTM in PyTorch.

Minimal, clean example of lstm neural network training in python, for learning purposes.

Python implementation of Empirical Mode Decompoisition (EMD) method

About Code release for "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" (ICLR 2023), https://openreview.net/pdf?id=ju_Uqw384Oq

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

A Library for Advanced Deep Time Series Models for General Time Series Analysis.

时序基础模型TimesNet，在长时、短时预测、缺失值填补、异常检测、分类五大任务上实现了全面领先。

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Electricity load forecasting using different deep learning algorithms

Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-sc…

Implementation of Electric Load Forecasting Based on CNN.

Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.

Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.

使用PYTorch框架建立的一个简单的LSTM模型来进行电力负荷预测

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models