Time Series Forecasting and Deep Learning

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
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.
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
- 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.

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Time Series Forecasting and Deep Learning

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