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.

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.

MSGARCH R Package

Jupyter Notebooks Collection for Learning Time Series Models

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

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.