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On this page
  • LSTM for Time Series Forecasting
  • Machine Learning for Multivariate Input
  • Statistical Method for Multivariate Input
  • Machine Learning for Univariate Input
  • Statistical Method for Univariate Input
  • Jupyter Notebook Examples
  • Univariate ARIMA
  • Univariate LSTM
  • Multivariate VAR
  • Multivariate LSTM
  • Prophet and Kats from Facebook
  • Note on Multivariate and Univariate
  • Software
  • Other Time Series
  • Precipitation Forecasting
  • Deep Learning for Forecasting
  • eBook Forecasting
  • Timeseries Forecasting
  • Timeseries Forecasting Book
  • Timeseries Forecasting Reading
  • Timeseries RNN
  • Timeseries Forecasting
  • Time-series Forecasting
  • VAR
  • time Series
  • LSTM
  • Time Series Toolbox
  • Books
  • Forecasting Comparison

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  1. Research

Time Series Forecasting

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Last updated 2 years ago

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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

  • , ,

  • , ,

  • : MPI-MSO

  • : LSTM

  • : ANFIS

  • Dataset : Electricity Load ENTSO, Model : LSTM, Type: Multivariate

  • , Dataset: river flow , 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

Machine Learning for Univariate Input

Statistical Method for Univariate Input

Jupyter Notebook Examples

Univariate ARIMA

import statsmodels

Univariate LSTM

import keras

Multivariate VAR

Multivariate LSTM

Prophet and Kats from Facebook

Note on Multivariate and Univariate

Software

Other Time Series

Precipitation Forecasting

  • Nowcasting, Upsampling, Interpolation, Super resolution

Deep Learning for Forecasting

top open source deep learning for time series forecasting frameworks.

eBook Forecasting

Timeseries Forecasting

Timeseries Forecasting Book

Timeseries Forecasting Reading

Timeseries RNN

Timeseries Forecasting

Time-series Forecasting

VAR

time Series

LSTM

Time Series Toolbox

  • 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

  • State of the art is 1D convnets, bleeding edge is transformers.

  • pycaret timeseries

  • lgbm light gbm

  • cvxpy

  • TensorFlow's LSTMCell

  • sktime

  • bssts

  • statsmodels

Books

Forecasting Comparison

: VAR

: VAR

: VAR

: VAR

: VAR

: NN

: LSTM

Dataset: Sunspot Zurich, Model: LSTM

Dataset: Air Pollution, Model: LSTM

Dataset: Stock Market, Model: LSTM, RF

Dataset: Amazon Stock Model: LSTM with addition

Dataset: Electric Consumption Model: LSTM

Dataset: Electricity Consumption Model: LSTM

Dataset: ENSO El Nino, Model: LSTM

Dataset: Sensor, Model: LSTM

Dataset: Solar power, Model: LSTM

Dataset: English power consumption, Model: ANP-RNN "Recurrent Attentive Neural Process for Sequential Data", ANP: Attentive Neural Processes, NP: Neural Processes, LSTM

, Dataset: Kaggle energy consuption, Model: LSTM, Seq2Seq

: LSTM, ARIMA, Prophet

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

Dataset: Daily Temperature, Model: LSTM

Dataset: Pollution, Solar Energy, Traffic data etec. Model MLP,RNN,LSTM,GRU, ARIMA, SVR, RF and TSR-RNN

Dataset: ?? Model: ARIMA, VAR, LSTM

Dataset: Olypic, LeBron, Zika, Model: ARIMA dan LSTM

Dataset: Power usage, Model: linear regression, random forest, support vector machine, BP neural network, GRU, LSTM

Dataset: precipitation, Model: ARIMA, LSTM, FNN(Feed forward Neural Networks), TLNN(Time lagged Neural Networks), SANN(Seasonal Artificial Neural Networks

(Note: VAR should only for )

: VAR

: VAR

: VAR

: VAR

: VAR

,

Collection of papers

One stop shop for time series analysis in Python

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.

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.

This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.

Another framework, written in PyTorch, this repository focuses more on probabilistic models. The repository isn’t that active (last commit was in November).

engineering statistics

Prophet

in R

in Python

in R

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.

# Statistical vs Deep Learning forecasting methods

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
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
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
Comparison for Debutanizer Column
dafrie/lstm-load-forecasting: Electricity load forecasting with LSTM (Recurrent Neural Network)
AIStream-Peelout/flow-forecast: Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
FlowDB Dataset - Flow Forecast - Flow Forecast
Multivariate Time Series - Vector Auto Regression (VAR)
Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - ML+
shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below.
Vector Autoregressive for Forecasting Time Series - by Sarit Maitra - Towards Data Science
A Real-World Application of Vector Autoregressive (VAR) model - by Mohammad Masum - Towards Data Science
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)
Time Series Forecasting with the Long Short-Term Memory Network in Python
rishikksh20/LSTM-Time-Series-Analysis: Using LSTM network for time series forecasting
sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM: Time Series Forecasting using LSTM in Keras.
pushpendughosh/Stock-market-forecasting: Forecasting directional movements of stock prices for intraday trading using LSTM and random forest
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.
demmojo/lstm-electric-load-forecast: Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network
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.
Yongyao/enso-forcasting: Improving the forecasting accuracy of ENSO through deep learning
EsmeYi/time-series-forcasting: Using K-NN, SVM, Bayes, LSTM, and multi-variable LSTM models on time series forecasting
CynthiaKoopman/Forecasting-Solar-Energy: Forecasting Solar Power: Analysis of using a LSTM Neural Network
3springs/attentive-neural-processes: implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
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.
Time Series Forecasting—ARIMA, LSTM, Prophet with Python - by Caner Dabakoglu - Medium
pyaf/load_forecasting: Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
jiegzhan/time-series-forecasting-rnn-tensorflow: Time series forecasting
zhangxu0307/time_series_forecasting_pytorch: time series forecasting using pytorch,including ANN,RNN,LSTM,GRU and TSR-RNN,experimental code
rakshita95/DeepLearning-time-series: LSTM for time series forecasting
mborysiak/Time-Series-Forecasting-with-ARIMA-and-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.
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.
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
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
Stationary process - Wikipedia
Multivariate Time Series - Vector Auto Regression (VAR)
Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - ML+
shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below.
Vector Autoregressive for Forecasting Time Series - by Sarit Maitra - Towards Data Science
A Real-World Application of Vector Autoregressive (VAR) model - by Mohammad Masum - Towards Data Science
How to Develop LSTM Models for Time Series Forecasting
Multi-Step LSTM Time Series Forecasting Models for Power Usage
Multivariate Time Series Forecasting with LSTMs in Keras
vb100/multivariate-lstm
Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM
How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution
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
ourownstory/neural_prophet: NeuralProphet: A simple forecasting package
NeuralProphet: The neural evolution of Meta’s Prophet
On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting
A Comparative Study between Univariate and Multivariate Linear Stationary Time Series Models
Alro10/deep-learning-time-series: List of papers, code and experiments using deep learning for time series forecasting
Gekko Timeseries and Modeling Software - Timeseries handling, and solving of large-scale economic models
Time Series Forecasting with Regression and LSTM - Paperspace Blog
Kats - Kats
chlubba/catch22: catch-22: CAnonical Time-series CHaracteristics
blue-yonder/tsfresh: Automatic extraction of relevant features from time series:
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"
Gluon
Flow Forecast
sktime dl
PyTorch-TS
Forecasting: Principles and Practice (2nd ed)
Forecasting: Principles and Practice (3rd ed)
Time Series Analysis and Its Applications: With R Examples - tsa4
nickpoison/tsa4: R code for Time Series Analysis and Its Applications, Ed 4
Time Series: A Data Analysis Approach Using R
NIST/SEMATECH e-Handbook of Statistical Methods
linkedin/greykite: A flexible, intuitive and fast forecasting library
alan-turing-institute/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.
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 High-Frequency Finance: 9780122796715: Gençay, Ramazan, Dacorogna, Michel, Muller, Ulrich A., Pictet, Olivier, Olsen, Richard: Books
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 Time-Series 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
Spice.ai
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
Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.
statsforecast - Statistical ⚡️ Forecast
Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - Machine Learning Plus
Time-series Analysis with VAR & VECM: Statistical approach - by Sarit Maitra - Towards Data Science
Vector Auto-regression Time series Model - by seemakurthi teja - Medium
Vector autoregression models
A vector autoregression weather model for electricity supply and demand modeling - SpringerLink
Time-Series 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 on Pytorch
Prophet | Forecasting at scale.
Forecasting Models for Tidy Time Series • fable
unit8co/darts: A python library for easy manipulation and forecasting of time series.
CRAN - Package forecast
robjhyndman/forecast: forecast package for R
tsfresh—tsfresh 0.18.1.dev39+g611e04f documentation
MiniRocket—sktime documentation
Demand forecasting with the Temporal Fusion Transformer—pytorch-forecasting documentation
https://github.com/fraunhoferportugal/tsfel
Forecasting: Principles and Practice (2nd ed)
statsforecast/experiments/m3 at main · Nixtla/statsforecast
Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.