Projects sharing features with PatchTST

Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement. The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability. The framework covers a broad range of analysis capabilities, includi

This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque

Implementation of the paper "Performative Time-Series Forecasting."

Implementation of the paper "LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting."

We require you to have anaconda or miniconda installed. Run the script ./scripts/setup.sh to setup the virtual environment with all the required packages.

1. Install requirements. `pip install -r requirements.txt`

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(NeurIPS 2024 Spotlight) 🌟 Paper Link

This is a Official PyTorch implementation of CLCRN in the following paper:

Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting, ICLR 2023

Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It enables the prediction of future values across diverse datasets without requiring task-specific training or optimization. The system functions as a probabilistic forecasting tool, producing multiple future trajectories and quantile forecasts to quantify uncertainty and potential prediction errors. It incorporates exogenous covariate integration to merge external variables and historical context into the input stream for increased precision. The project includes utilities for sy

Dependencies can be installed using the following file: newtimelibenvironment.yml You can obtain the well pre-processed datasets from [Google Drive](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing) or [[Baidu…

SC data: the dataset contains power grid series of 133 locations, and the location index, date, hour, temperature, precipitation, active power and reactive power is reported in the dataset.

Code for our SIGKDD'25 paper "Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates"

(AAAI'25) TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

This is the official implementation of "Neural Conformal Control for Time Series Forecasting" (NCC) appearing in AAAI 2025 (main track). Authors are Ruipu Li and Alexander Rodríguez from the University of Michigan.

The repo is the official implementation for the paper: CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations (ICML'25).

This is the original pytorch implementation for the following paper: SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. Alse see the Open Review verision.

Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin, "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting," in Proc. 39th International Conference on Machine Learning (ICML 2022), Baltimore, Maryland, July 17-23, 2022. paper

Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun, "Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting" in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), 2022. paper

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting https://arxiv.org/abs/2205.08897

Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin, "One Fits All: Power General Time Series Analysis by Pretrained LM,", NeurIPS, 2023. paper

AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

This is the official repo for "Auto-Regressive Moving Diffusion Models for Time Series Forecasting".

This is the Pytorch implementation of Pyraformer (Pyramidal Attention based Transformer) in the ICLR paper: Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting.