Time Series Forecasting

LSTM for Time Series Forecasting

Univariate LSTM Models : one observation time-series data, predict the next value in the sequence
Multivariate LSTM Models : two or more observation time-series 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 Multi-Step LSTM Models : one observation time-series data, predict the multi step value in the sequence prediction.
Multivariate Multi-Step LSTM Models : two or more observation time-series data, predict the multi step value in the sequence prediction.
- Multiple Input Multi-Step Output.
- Multiple Parallel Input and Multi-Step Output.

Machine Learning for Multivariate Input

How to Develop LSTM Models for Time Series Forecasting
Multi-Step LSTM Time Series Forecasting Models for Power Usage, dhamvi01/Multivariate-Time-Series-Using-LSTM, ManishPrajapat/Household-Energy-MultiVariate-LSTM-: Data - Multivariate time series data of a house is provided
Multivariate Time Series Forecasting with LSTMs in Keras, dhairya0904/Multivariate-time-series-prediction: Multivariate time series prediction using LSTM in keras, rubel007cse/Multivariate-Time-Series-Forecasting: Multivariate Time Series Forecasting with LSTMs in Keras
vb100/multivariate-lstm

[Multivariate Time Series Forecasting with a Bidirectional LSTM: Building a Model Geared to Multiple Input Series

by Pierre Beaujuge

Medium](https://medium.com/@pierre.beaujuge/multivariate-time-series-forecasting-with-a-bidirectional-lstm-building-a-model-geared-to-4f020a160636)

umbertogriffo/Predictive-Maintenance-using-LSTM: Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
[Time series forecasting TensorFlow Core](https://www.tensorflow.org/tutorials/structured_data/time_series) : MPI-MSO
shrey920/MultivariateTimeSeriesForecasting: This project is an implementation of the paper Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. The model LSTNet consists of CNN, LSTM and RNN-skip layers
Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM
How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution
AnoML/multivariate-timeseries-forecasting: A set of algorithms using for Multivariate Time-Series Forecasting : LSTM
Comparison for Debutanizer Column : ANFIS
dafrie/lstm-load-forecasting: Electricity load forecasting with LSTM (Recurrent Neural Network) Dataset : Electricity Load ENTSO, Model : LSTM, Type: Multivariate
AIStream-Peelout/flow-forecast: 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 Multi-Head Attention, Transformer with a linear decoder, DA-RNN, Transformer XL, Informer, DeepAR

Statistical Method for Multivariate Input

[Multivariate Time Series Vector Auto Regression (VAR)](https://www.analyticsvidhya.com/blog/2018/09/multivariate-time-series-guide-forecasting-modeling-python-codes/) : VAR
Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - ML+ : VAR
shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below. : VAR
[Vector Autoregressive for Forecasting Time Series by Sarit Maitra Towards Data Science](https://towardsdatascience.com/vector-autoregressive-for-forecasting-time-series-a60e6f168c70) : VAR
[A Real-World Application of Vector Autoregressive (VAR) model by Mohammad Masum Towards Data Science](https://towardsdatascience.com/multivariate-time-series-forecasting-456ace675971) : VAR
ZahraNabilaIzdihar/Backpropagation-Neural-Network-for-Multivariate-Time-Series-Forecasting: 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 Short-Term Memory Network in Python : LSTM
rishikksh20/LSTM-Time-Series-Analysis: Using LSTM network for time series forecasting Dataset: Sunspot Zurich, Model: LSTM
sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM: Time Series Forecasting using LSTM in Keras. Dataset: Air Pollution, Model: LSTM
pushpendughosh/Stock-market-forecasting: Forecasting directional movements of stock prices for intraday trading using LSTM and random forest Dataset: Stock Market, Model: LSTM, RF
deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-: 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/lstm-electric-load-forecast: Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network Dataset: Electric Consumption Model: LSTM
Yifeng-He/Electric-Power-Hourly-Load-Forecasting-using-Recurrent-Neural-Networks: 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/enso-forcasting: Improving the forecasting accuracy of ENSO through deep learning Dataset: ENSO El Nino, Model: LSTM
EsmeYi/time-series-forcasting: Using K-NN, SVM, Bayes, LSTM, and multi-variable LSTM models on time series forecasting Dataset: Sensor, Model: LSTM
CynthiaKoopman/Forecasting-Solar-Energy: Forecasting Solar Power: Analysis of using a LSTM Neural Network Dataset: Solar power, Model: LSTM
3springs/attentive-neural-processes: implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout) Dataset: English power consumption, Model: ANP-RNN "Recurrent Attentive Neural Process for Sequential Data", ANP: Attentive Neural Processes, NP: Neural Processes, LSTM
Housiadas/forecasting-energy-consumption-LSTM: 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](https://medium.com/@cdabakoglu/time-series-forecasting-arima-lstm-prophet-with-python-e73a750a9887) : 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/time-series-forecasting-rnn-tensorflow: Time series forecasting Dataset: Daily Temperature, Model: LSTM
zhangxu0307/time_series_forecasting_pytorch: time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code Dataset: Pollution, Solar Energy, Traffic data etec. Model MLP,RNN,LSTM,GRU, ARIMA, SVR, RF and TSR-RNN
rakshita95/DeepLearning-time-series: LSTM for time series forecasting Dataset: ?? Model: ARIMA, VAR, LSTM
mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM Dataset: Olypic, LeBron, Zika, Model: ARIMA dan LSTM
stxupengyu/load-forecasting-algorithms: 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/Time-Series-Forecasting: 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 Out-of-Sample Forecasts with ARIMA in Python
rakshita95/DeepLearning-time-series: LSTM for time series forecasting
Abhishekmamidi123/Time-Series-Forecasting: 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/Time-Series-Forecasting-with-ARIMA-and-LSTM
[Time Series Forecasting — ARIMA, LSTM, Prophet with Python by Caner Dabakoglu Medium](https://medium.com/@cdabakoglu/time-series-forecasting-arima-lstm-prophet-with-python-e73a750a9887)

Univariate LSTM

import keras

How to Develop LSTM Models for Time Series Forecasting
rakshita95/DeepLearning-time-series: LSTM for time series forecasting
Abhishekmamidi123/Time-Series-Forecasting: 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/Time-Series-Forecasting-with-ARIMA-and-LSTM
Time Series Forecasting with the Long Short-Term Memory Network in Python
[Time Series Forecasting — ARIMA, LSTM, Prophet with Python by Caner Dabakoglu Medium](https://medium.com/@cdabakoglu/time-series-forecasting-arima-lstm-prophet-with-python-e73a750a9887)

Multivariate VAR

(Note: VAR should only for Stationary process - Wikipedia)

[Multivariate Time Series Vector Auto Regression (VAR)](https://www.analyticsvidhya.com/blog/2018/09/multivariate-time-series-guide-forecasting-modeling-python-codes/) : VAR
Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - ML+ : VAR
shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below. : VAR
[Vector Autoregressive for Forecasting Time Series by Sarit Maitra Towards Data Science](https://towardsdatascience.com/vector-autoregressive-for-forecasting-time-series-a60e6f168c70) : VAR
[A Real-World Application of Vector Autoregressive (VAR) model by Mohammad Masum Towards Data Science](https://towardsdatascience.com/multivariate-time-series-forecasting-456ace675971) : VAR

Multivariate LSTM

Prophet and Kats from Facebook

Time-Series Forecasting with Facebook Prophet and OmniSci
Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy?, HN Discussion
[Kats Kats](https://facebookresearch.github.io/Kats/)

Note on Multivariate and Univariate

Software

Gekko Timeseries and Modeling Software - Timeseries handling, and solving of large-scale economic models

Other Time Series

[Time Series Forecasting with Regression and LSTM Paperspace Blog](https://blog.paperspace.com/time-series-forecasting-regression-and-lstm/?ref=mlnews)
[Kats Kats](https://facebookresearch.github.io/Kats/) One stop shop for time series analysis in Python
chlubba/catch22: catch-22: CAnonical Time-series CHaracteristics
blue-yonder/tsfresh: Automatic extraction of relevant features from time series:

Precipitation Forecasting

Nowcasting, Upsampling, Interpolation, Super resolution
hydrogo/rainnet: RainNet: a convolutional neural network for radar-based precipitation nowcasting
1706.03458 Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
- sxjscience/HKO-7: Source code of paper "NIPS2017 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 lock-in 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.
PyTorch-TS Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn't that active (last commit was in November).