forecasting

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"

Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from ServiceNow Research

Access to Sulie foundation models for time-series forecasting 📈

A professional list on Multi-Modalities For Time Series Analysis (MM4TSA) Papers and Resource.

This repository contains the implementation of paper Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting with different loss functions in Tensorflow. We have compared 14 regression loss functions performance on 4 different datasets.

This includes the codebase for EntroPE (Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting) paper.

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily

Multi-scale Finetuning for Encoder-based Time Series Foundation Models