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.

Applications
Papers
- 2024
- 2023
- 2022
- 2021
- 2020
- 2019
- 2018
- 2017
Blogs
Competitions
Courses
Libraries
Datasets
Books
Repositories
Tutorials

Applications

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

Papers

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.
functime
- functime is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets.
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.
pyFTS
- An open source library for Fuzzy Time Series in Python.
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
- tft-torch is a Python library that implements "Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting" using pytorch framework.
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

awesome-time-series
- List of state of the art papers, code, and other resources focus on time series forecasting.
awesome_time_series_in_python
- This curated list contains python packages for time series analysis.
DL4Stock
- This is the repository for the collection of deep learning in stock market prediction.
pytorch-transformer-ts
- Repository of Transformer based PyTorch Time Series Models.
Transformers for Time Series
- Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series (Powered by PyTorch).

Tutorials

Deep Learning and Machine Learning for Stock Predictions
- This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML).
Feature Engineering for Time Series Forecasting
- Create lag, window and seasonal features, perform imputation, variable encoding, extract features from datetime, remove outliers, and more.
Stock-Prediction-Models
- Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.
time-series-forecasting-with-python
- A use-case focused tutorial for time series forecasting with python.

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

readme

Time Series Forecasting and Deep Learning

Table of Contents

Applications

Papers

2024

2023

2022

2021

2020

2019

2018

2017