/Time-Series-Forecasting-and-Deep-Learning

Resources about time series forecasting and deep learning.

Time Series Forecasting and Deep Learning

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List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.

Table of Contents

Applications

  • TimeGPT

    • Nixtla’s TimeGPT is a generative pre-trained forecasting model for time series data.

Papers

2023

2022

2021

2020

2019

2018

2017

Blogs

Competitions

Courses

Libraries

  • arch

    • Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)

  • AutoGP.jl

    • A Julia package for learning the covariance structure of Gaussian process time series models.

  • AutoTS

    • AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.

  • BasicTS

    • BasicTS (Basic Time Series) is a PyTorch-based benchmark and toolbox for time series forecasting (TSF).

  • Beibo

    • Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.

  • Cesium

    • Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions.

  • Darts

    • Darts is a Python library for easy manipulation and forecasting of time series.

  • DeepOD

    • DeepOD is an open-source python framework for deep learning-based anomaly detection on multivariate data.

  • Flow Forecast

    • Flow Forecast is a deep learning PyTorch library for time series forecasting, classification, and anomaly detection.

  • GluonTS

    • GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models.

  • Greykite

    • The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

  • HyperTS

    • A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

  • Kats

    • Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

  • Luminaire

    • Luminaire is a python package that provides ML-driven solutions for monitoring time series data.

  • MAPIE

    • A scikit-learn-compatible module for estimating prediction intervals.

  • Merlion

    • Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance.

  • NeuralForecast

    • NeuralForecast is a Python library for time series forecasting with deep learning models.

  • NeuralProphet

    • NeuralProphet is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Network and traditional time-series algorithms, inspired by Facebook Prophet and AR-Net.

  • PaddleTS

    • PaddlePaddle-based Time Series Modeling in Python.

  • Prophet

    • Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.

  • Puncc

    • Puncc is a python library for predictive uncertainty quantification using conformal prediction.

  • PyBATS

    • PyBATS is a package for Bayesian time series modeling and forecasting.

  • PyDaddy

    • A Python package to discover stochastic differential equations from time series data.

  • PyDMD: Python Dynamic Mode Decomposition

    • PyDMD is a Python package that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.

  • PyPOTS

    • A Python Toolbox for Data Mining on Partially-Observed Time Series.

  • Python Outlier Detection (PyOD)

    • PyOD is a comprehensive and scalable Python library for outlier detection (anomaly detection)

  • PyTorch Forecasting

    • PyTorch Forecasting is a PyTorch-based package for forecasting time series with state-of-the-art network architectures.

  • PyTorchTS

    • PyTorchTS is a PyTorch Probabilistic Time Series forecasting framework which provides state of the art PyTorch time series models by utilizing GluonTS as its back-end API and for loading, transforming and back-testing time series data sets.

  • pytrendseries

    • pytrendseries is a Python library for detection of trends in time series like: stock prices, monthly sales, daily temperature of a city and so on.

  • pyts

    • pyts is a Python package dedicated to time series classification.

  • Qlib

    • Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

  • RQAlpha

    • A extendable, replaceable Python algorithmic backtest & trading framework supporting multiple securities.

  • Scalecast

    • The pratictioner's forecasting library. Including automated model selection, model optimization, pipelines, visualization, and reporting.

  • sequitur

    • sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code.

  • skforecast

    • Skforecast is a Python library that eases using scikit-learn regressors as single and multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (LightGBM, XGBoost, CatBoost, ...).

  • sktime

    • sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks.

  • StatsForecast

    • StatsForecast offers a collection of popular univariate time series forecasting models optimized for high performance and scalability.

  • TFTS

    • TFTS (TensorFlow Time Series) is an easy-to-use python package for time series, supporting the classical and SOTA deep learning methods in TensorFlow or Keras.

  • tft-torch

  • TimeEval

    • TimeEval is an evaluation tool for time series anomaly detection algorithms.

  • Time Interpret (tint)

    • This library expands the Captum library with a specific focus on time-series.

  • Time Series Library (TSlib)

    • TSlib is an open-source library for deep learning researchers, especially deep time series analysis.

  • TODS

    • TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

  • transdim

    • Machine learning for transportation data imputation and prediction.

  • tsai

    • tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation...

  • tsam

    • tsam is a python package which uses different machine learning algorithms for the aggregation of time series.

  • tsaug

    • tsaug is a Python package for time series augmentation.

  • TSDB

    • A Python Toolbox to Ease Loading Open-Source Time-Series Datasets.

  • tsfeatures

    • Calculates various features from time series data. Python implementation of the R package tsfeatures.

  • TSFEL

    • Time Series Feature Extraction Library (TSFEL for short) is a Python package for feature extraction on time series data.

  • tsfresh

    • tsfresh provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm.

  • tslearn

    • tslearn is a Python package that provides machine learning tools for the analysis of time series.

  • tspiral

    • A python package for time series forecasting with scikit-learn estimators.

Datasets

Books

  • Forecasting: Principles and Practice (3rd ed)

    • Rob J Hyndman and George Athanasopoulos, 2021

    • This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.

Repositories

Tutorials