Time Series Forecasting
LSTM for Time Series Forecasting
 Univariate LSTM Models : one observation timeseries data, predict the next value in the sequence
 Multivariate LSTM Models : two or more observation timeseries data, predict the next value in the sequence
 Multiple Input Series : two or more parallel input time series and an output time series that is dependent on the input time series
 Multiple Parallel Series : multiple parallel time series and a value must be predicted for each
 Univariate MultiStep LSTM Models : one observation timeseries data, predict the multi step value in the sequence prediction.
 Multivariate MultiStep LSTM Models : two or more observation timeseries data, predict the multi step value in the sequence prediction.
 Multiple Input MultiStep Output.
 Multiple Parallel Input and MultiStep Output.
Machine Learning for Multivariate Input
 How to Develop LSTM Models for Time Series Forecasting
 MultiStep LSTM Time Series Forecasting Models for Power Usage, dhamvi01/MultivariateTimeSeriesUsingLSTM, ManishPrajapat/HouseholdEnergyMultiVariateLSTM: Data  Multivariate time series data of a house is provided
 Multivariate Time Series Forecasting with LSTMs in Keras, dhairya0904/Multivariatetimeseriesprediction: Multivariate time series prediction using LSTM in keras, rubel007cse/MultivariateTimeSeriesForecasting: Multivariate Time Series Forecasting with LSTMs in Keras
 vb100/multivariatelstm
 Multivariate Time Series Forecasting with a Bidirectional LSTM: Building a Model Geared to Multiple Input Series  by Pierre Beaujuge  Medium
 umbertogriffo/PredictiveMaintenanceusingLSTM: Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
 Time series forecasting  TensorFlow Core : MPIMSO
 shrey920/MultivariateTimeSeriesForecasting: This project is an implementation of the paper Modeling Long and ShortTerm Temporal Patterns with Deep Neural Networks. The model LSTNet consists of CNN, LSTM and RNNskip layers
 ShivKumarYadav9/StockPricePredictionbyMultivariateMultistepLSTM
 How to Develop Multivariate MultiStep Time Series Forecasting Models for Air Pollution
 AnoML/multivariatetimeseriesforecasting: A set of algorithms using for Multivariate TimeSeries Forecasting : LSTM
 Comparison for Debutanizer Column : ANFIS
 dafrie/lstmloadforecasting: Electricity load forecasting with LSTM (Recurrent Neural Network) Dataset : Electricity Load ENTSO, Model : LSTM, Type: Multivariate
 AIStreamPeelout/flowforecast: Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting)., Dataset: river flow FlowDB Dataset  Flow Forecast  Flow Forecast, flood severity, Model: LSTM, Transformer, Simple MultiHead Attention, Transformer with a linear decoder, DARNN, Transformer XL, Informer, DeepAR
Statistical Method for Multivariate Input
 Multivariate Time Series  Vector Auto Regression (VAR) : VAR
 Vector Autoregression (VAR)  Comprehensive Guide with Examples in Python  ML+ : VAR
 shraddhaan/timeseriesgsr: Multivariate TimeSeries Forecasting of Gas Sensor Array Readings. Accompanying Medium article below. : VAR
 Vector Autoregressive for Forecasting Time Series  by Sarit Maitra  Towards Data Science : VAR
 A RealWorld Application of Vector Autoregressive (VAR) model  by Mohammad Masum  Towards Data Science : VAR
 ZahraNabilaIzdihar/BackpropagationNeuralNetworkforMultivariateTimeSeriesForecasting: Backpropagation Neural Network for Multivariate Time Series Forecasting (multi input single output: 2 inputs and 1 output) : NN
Machine Learning for Univariate Input
 Time Series Forecasting with the Long ShortTerm Memory Network in Python : LSTM
 rishikksh20/LSTMTimeSeriesAnalysis: Using LSTM network for time series forecasting Dataset: Sunspot Zurich, Model: LSTM
 sagarmk/ForecastingonAirpollutionwithRNNLSTM: Time Series Forecasting using LSTM in Keras. Dataset: Air Pollution, Model: LSTM
 pushpendughosh/Stockmarketforecasting: Forecasting directional movements of stock prices for intraday trading using LSTM and random forest Dataset: Stock Market, Model: LSTM, RF
 deshpandenu/TimeSeriesForecastingofAmazonStockPricesusingNeuralNetworksLSTMandGAN: Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator. Dataset: Amazon Stock Model: LSTM with addition
 demmojo/lstmelectricloadforecast: Electric load forecast using LongShortTermMemory (LSTM) recurrent neural network Dataset: Electric Consumption Model: LSTM
 YifengHe/ElectricPowerHourlyLoadForecastingusingRecurrentNeuralNetworks: This project aims to predict the hourly electricity load in Toronto based on the loads of previous 23 hours using LSTM recurrent neural network. Dataset: Electricity Consumption Model: LSTM
 Yongyao/ensoforcasting: Improving the forecasting accuracy of ENSO through deep learning Dataset: ENSO El Nino, Model: LSTM
 EsmeYi/timeseriesforcasting: Using KNN, SVM, Bayes, LSTM, and multivariable LSTM models on time series forecasting Dataset: Sensor, Model: LSTM
 CynthiaKoopman/ForecastingSolarEnergy: Forecasting Solar Power: Analysis of using a LSTM Neural Network Dataset: Solar power, Model: LSTM
 3springs/attentiveneuralprocesses: implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout) Dataset: English power consumption, Model: ANPRNN "Recurrent Attentive Neural Process for Sequential Data", ANP: Attentive Neural Processes, NP: Neural Processes, LSTM
 Housiadas/forecastingenergyconsumptionLSTM: Development of a machine learning application for IoT platform to predict electric energy consumption in smart building environment in real time., Dataset: Kaggle energy consuption, Model: LSTM, Seq2Seq
Statistical Method for Univariate Input
 Time Series Forecasting—ARIMA, LSTM, Prophet with Python  by Caner Dabakoglu  Medium : LSTM, ARIMA, Prophet
 pyaf/load_forecasting: Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models Dataset: Electricity, Model: Feed forward Neural Network FFNN, Simple Moving Average SMA, Weighted Moving Average WMA,
Simple Exponential Smoothing SES, Holts Winters HW, Autoregressive Integrated Moving Average ARIMA, Recurrent Neural Networks RNN, Long Short Term Memory cells LSTM, Gated Recurrent Unit cells GRU, Type: Univariate  jiegzhan/timeseriesforecastingrnntensorflow: Time series forecasting Dataset: Daily Temperature, Model: LSTM
 zhangxu0307/time_series_forecasting_pytorch: time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSRRNN，experimental code Dataset: Pollution, Solar Energy, Traffic data etec.
Model MLP,RNN,LSTM,GRU, ARIMA, SVR, RF and TSRRNN  rakshita95/DeepLearningtimeseries: LSTM for time series forecasting Dataset: ?? Model: ARIMA, VAR, LSTM
 mborysiak/TimeSeriesForecastingwithARIMAandLSTM Dataset: Olypic, LeBron, Zika, Model: ARIMA dan LSTM
 stxupengyu/loadforecastingalgorithms: 使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vector machine, BP neural network, GRU, LSTM) are used for power system load forecasting / power forecasting. Dataset: Power usage, Model: linear regression, random forest, support vector machine, BP neural network, GRU, LSTM
 Abhishekmamidi123/TimeSeriesForecasting: Rainfall analysis of Maharashtra  Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons. Dataset: precipitation, Model: ARIMA, LSTM, FNN(Feed forward Neural Networks), TLNN(Time lagged Neural Networks), SANN(Seasonal Artificial Neural Networks
Jupyter Notebook Examples
Univariate ARIMA
import statsmodels
 How to Create an ARIMA Model for Time Series Forecasting in Python
 How to Make Manual Predictions for ARIMA Models with Python
 How to Make OutofSample Forecasts with ARIMA in Python
 rakshita95/DeepLearningtimeseries: LSTM for time series forecasting
 Abhishekmamidi123/TimeSeriesForecasting: Rainfall analysis of Maharashtra  Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.
 mborysiak/TimeSeriesForecastingwithARIMAandLSTM
 Time Series Forecasting—ARIMA, LSTM, Prophet with Python  by Caner Dabakoglu  Medium
Univariate LSTM
import keras
 How to Develop LSTM Models for Time Series Forecasting
 rakshita95/DeepLearningtimeseries: LSTM for time series forecasting
 Abhishekmamidi123/TimeSeriesForecasting: Rainfall analysis of Maharashtra  Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.
 mborysiak/TimeSeriesForecastingwithARIMAandLSTM
 Time Series Forecasting with the Long ShortTerm Memory Network in Python
 Time Series Forecasting—ARIMA, LSTM, Prophet with Python  by Caner Dabakoglu  Medium
Multivariate VAR
(Note: VAR should only for Stationary process  Wikipedia)
 Multivariate Time Series  Vector Auto Regression (VAR) : VAR
 Vector Autoregression (VAR)  Comprehensive Guide with Examples in Python  ML+ : VAR
 shraddhaan/timeseriesgsr: Multivariate TimeSeries Forecasting of Gas Sensor Array Readings. Accompanying Medium article below. : VAR
 Vector Autoregressive for Forecasting Time Series  by Sarit Maitra  Towards Data Science : VAR
 A RealWorld Application of Vector Autoregressive (VAR) model  by Mohammad Masum  Towards Data Science : VAR
Multivariate LSTM
 How to Develop LSTM Models for Time Series Forecasting
 MultiStep LSTM Time Series Forecasting Models for Power Usage
 Multivariate Time Series Forecasting with LSTMs in Keras
 vb100/multivariatelstm
 ShivKumarYadav9/StockPricePredictionbyMultivariateMultistepLSTM
 How to Develop Multivariate MultiStep Time Series Forecasting Models for Air Pollution
Prophet and Kats from Facebook
 TimeSeries Forecasting with Facebook Prophet and OmniSci
 Is Facebook's "Prophet" the TimeSeries Messiah, or Just a Very Naughty Boy?, HN Discussion
 Kats  Kats
 ourownstory/neural_prophet: NeuralProphet: A simple forecasting package
 NeuralProphet: The neural evolution of Meta's Prophet
Note on Multivariate and Univariate
 On the Suitability of Long ShortTerm Memory Networks for Time Series Forecasting
 A Comparative Study between Univariate and Multivariate Linear Stationary Time Series Models
 Alro10/deeplearningtimeseries: List of papers, code and experiments using deep learning for time series forecasting Collection of papers
Software
Other Time Series
 Time Series Forecasting with Regression and LSTM  Paperspace Blog
 Kats  Kats One stop shop for time series analysis in Python
 chlubba/catch22: catch22: CAnonical Timeseries CHaracteristics
 blueyonder/tsfresh: Automatic extraction of relevant features from time series:
Precipitation Forecasting
 Nowcasting, Upsampling, Interpolation, Super resolution
 hydrogo/rainnet: RainNet: a convolutional neural network for radarbased precipitation nowcasting
 1706.03458 Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Deep Learning for Forecasting
top open source deep learning for time series forecasting frameworks.
 Gluon This framework by Amazon remains one of the top DL based time series forecasting frameworks on GitHub. However, there are some down sides including lockin to MXNet (a rather obscure architecture). The repository also doesn't seem to be quick at adding new research.
 Flow Forecast This is an upcoming PyTorch based deep learning for time series forecasting framework. The repository features a lot of recent models out of research conferences along with an easy to use deployment API. The repository is one of the few repos to have new models, coverage tests, and interpretability metrics.
 sktime dl This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.
 PyTorchTS Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn't that active (last commit was in November).
eBook Forecasting
 Forecasting: Principles and Practice (2nd ed)
 Forecasting: Principles and Practice (3rd ed)
 Time Series Analysis and Its Applications: With R Examples  tsa4
 Time Series: A Data Analysis Approach Using R
 NIST/SEMATECH eHandbook of Statistical Methods engineering statistics
Timeseries Forecasting
 linkedin/greykite: A flexible, intuitive and fast forecasting library
 alanturinginstitute/sktime: A unified framework for machine learning with time series
 unit8co/darts: A python library for easy manipulation and forecasting of time series.
 Time Series Forecasting  Machine Learning  Amazon Forecast
 Prophet  Forecasting at scale.
Timeseries Forecasting Book
 Forecasting: Principles and Practice (2nd ed)
 Introduction to Time Series and Forecasting  SpringerLink
 Amazon.com: Practical Time Series Analysis: Prediction with Statistics and Machine Learning: 9781492041658: Nielsen, Aileen: Books
 Amazon.com: An Introduction to HighFrequency Finance: 9780122796715: Gençay, Ramazan, Dacorogna, Michel, Muller, Ulrich A., Pictet, Olivier, Olsen, Richard: Books
Timeseries Forecasting Reading
 Time Series Analysis and Forecasting with ARIMA  kanoki
 Makridakis Competitions  Wikipedia
 AileenNielsen/TimeSeriesAnalysisWithPython
 ARIMA Model  Complete Guide to Time Series Forecasting in Python  ML+
 Aileen Nielsen Time Series Analysis PyCon 2017  YouTube
 Time Series Analysis with Python Intermediate  SciPy 2016 Tutorial  Aileen Nielsen  YouTube
 Sorry ARIMA, but I'm Going Bayesian  Stitch Fix Technology–Multithreaded
 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)
 Hidden Markov Models  An Introduction  QuantStart
 Nixtla/nixtla: Automated time series processing and forecasting.
 Is Facebook's "Prophet" the TimeSeries Messiah, or Just a Very Naughty Boy?
 unit8co/darts: A python library for easy manipulation and forecasting of time series.
 Introduction—statsmodels
 Benchmarking Facebook's Prophet–Nikolaos Kourentzes
Timeseries RNN
Timeseries Forecasting
 Forecasting: Principles and Practice (3rd ed)
 Principles of Econometrics with R
 Introduction to Econometrics with R
 Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How  Databricks
 Economics 312 Reading List  Economics  Reed College
Timeseries Forecasting
 Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.
 statsforecast  Statistical ⚡️ Forecast
VAR
 Vector Autoregression (VAR)  Comprehensive Guide with Examples in Python  Machine Learning Plus
 Timeseries Analysis with VAR & VECM: Statistical approach  by Sarit Maitra  Towards Data Science
 Vector Autoregression Time series Model  by seemakurthi teja  Medium
 Vector autoregression models
 A vector autoregression weather model for electricity supply and demand modeling  SpringerLink
time Series
 TimeSeries Analysis Course
 Online DSS: Time Series Analysis for Business Forecasting with Python  Algoritma
 Time Series Analysis (and Forecasting)  Free Statistics and Forecasting Software (Calculators) v.1.2.1
 Time Series  solver
 Time Series Lab  Advanced Time Series Forecasting Software
 NCAR Command Language (NCL)
LSTM
Time Series Toolbox

Prophet [Prophet Forecasting at scale.](https://facebook.github.io/prophet/)  Forecasting Models for Tidy Time Series • fable in R
 unit8co/darts: A python library for easy manipulation and forecasting of time series. in Python
 CRAN  Package forecast in R robjhyndman/forecast: forecast package for R
 XGboost, LGBM, pmdarima, stanpy (for bayesian modelling)
 Prophet  seems to be the current ‘standard' choice
 ARIMA  Classical choice
 Exponential Moving Average  dead simple to implement, works well for stuff that's a time series but not very seasonal
 Kalman/Statespace model  used by Splunk's predict[1] command (pretty sure I always used LLP5)
 Prophet, statsmodels, tf.keras for RNNs.
 tensorflow probability's time series package.
 PyTorch for recurrent nets
 tsfresh—tsfresh 0.18.1.dev39+g611e04f documentation
 MiniRocket—sktime documentation
 State of the art is 1D convnets, bleeding edge is transformers.
 pycaret timeseries
 lgbm light gbm
 cvxpy
 TensorFlow's LSTMCell
 LSTMs have been going the way of the dinosaurs since 2018. If you really need a complex neural network (over 1D convolution approaches), transformers are the current SOTA. Demand forecasting with the Temporal Fusion Transformer—pytorchforecasting documentation
 sktime
 bssts
 statsmodels
 https://github.com/fraunhoferportugal/tsfel
Books
Forecasting Comparison
 statsforecast/experiments/m3 at main · Nixtla/statsforecast # Statistical vs Deep Learning forecasting methods
 Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.