List of papers, code and experiments using deep learning for time series forecasting

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

Time series analysis in the `tidyverse`

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

AtsPy: Automated Time Series Models in Python (by @firmai)

Streamlit app to train, evaluate and optimize a Prophet forecasting model.

An open source library for Fuzzy Time Series in Python

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

PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)

QGIS toolkit 🧰 for pre- and post-processing 🔨, visualizing 🔍, and running simulations 💻 in the Weather Research and Forecasting (WRF) model 🌀

Extending broom for time series forecasting

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

MSGARCH R Package

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.

Sky Cast: A Comparison of Modern Techniques for Forecasting Time Series

Jupyter Notebooks Collection for Learning Time Series Models

This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

spinesTS, a powerful toolset for time series prediction, is one of the cornerstones of PipelineTS.

Python based Quant Finance Models, Tools and Algorithmic Decision Making

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.