Projects sharing features with CondTSF

Implementation of the paper "Performative 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.

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

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

ROCKET · MINIROCKET · HYDRA

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

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

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(NeurIPS 2024 Spotlight) 🌟 Paper Link

sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran

tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones

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

This repo provides an implementation of the FC-GAGA algorithm introduced in https://arxiv.org/abs/2007.15531 and reproduces the experimental results presented in the paper.

This is a PyTorch implementation of the paper "Discrete Graph Structure Learning for Forecasting Multiple Time Series", ICLR 2021.

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

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.

(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).

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

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

Paper Link: https://arxiv.org/abs/2106.03904