Stars
This includes the codebase for EntroPE (Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting) paper.
[NeurIPS 2025 Spotlight] Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"
"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"
[AAAI 2026] Official implementation of "TimeMosaic: Information-Density Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding"
This is an official repository for "Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting".
A toolkit for time series machine learning and deep learning
MultiMAE: Multi-modal Multi-task Masked Autoencoders, ECCV 2022
A Python package for time series classification
The implementation of paper: IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction, ICML 2025
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
This is an official repository for "Harnessing Vision Models for Time Series Analysis: A Survey".
Code for our paper "VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters".
[AAAI 2025] Official implementation of "TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment"
[KDD 2025] Awesome Multi-modal Time Series Analysis
[ICML 2025] Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
[tnnls 2025] Language-guided contrastive learning for M/EEG-based image recognition.
A list of free LLM inference resources accessible via API.
A collection of dataset consists of a total of 8 English speech datasets for SER
[NeurIPS 2025 Spotlight] A Unified Tokenizer for Visual Generation and Understanding
A curated list of papers in the intersection of multimodal LLMs and time series analysis. https://mllm-ts.github.io/paper/MLLMTS_Survey.pdf