# 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](https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/)
* [Multi-Step LSTM Time Series Forecasting Models for Power Usage](https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/), [dhamvi01/Multivariate-Time-Series-Using-LSTM](https://github.com/dhamvi01/Multivariate-Time-Series-Using-LSTM), [ManishPrajapat/Household-Energy-MultiVariate-LSTM-: Data - Multivariate time series data of a house is provided](https://github.com/ManishPrajapat/Household-Energy-MultiVariate-LSTM-)
* [Multivariate Time Series Forecasting with LSTMs in Keras](https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/), [dhairya0904/Multivariate-time-series-prediction: Multivariate time series prediction using LSTM in keras](https://github.com/dhairya0904/Multivariate-time-series-prediction), [rubel007cse/Multivariate-Time-Series-Forecasting: Multivariate Time Series Forecasting with LSTMs in Keras](https://github.com/rubel007cse/Multivariate-Time-Series-Forecasting)
* [vb100/multivariate-lstm](https://github.com/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.](https://github.com/umbertogriffo/Predictive-Maintenance-using-LSTM)
* [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](https://github.com/shrey920/MultivariateTimeSeriesForecasting)
* [Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM](https://github.com/Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM)
* [How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution](https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/)
* [AnoML/multivariate-timeseries-forecasting: A set of algorithms using for Multivariate Time-Series Forecasting](https://github.com/AnoML/multivariate-timeseries-forecasting) : LSTM
* [Comparison for Debutanizer Column](http://www.vincentgaspersz.com/wp-content/uploads/2018/07/K%20Siddharth%20A%20Pathak%20AK%20Pani%20-%20Real-time%20quality%20monitoring%20in%20debutanizer%20column%20with%20regression%20tree%20and%20ANFIS.pdf) : ANFIS
* [dafrie/lstm-load-forecasting: Electricity load forecasting with LSTM (Recurrent Neural Network)](https://github.com/dafrie/lstm-load-forecasting) 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).](https://github.com/AIStream-Peelout/flow-forecast), Dataset: river flow [FlowDB Dataset - Flow Forecast - Flow Forecast](https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/33456135/FlowDB+Dataset), 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+](https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/) : VAR
* [shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below.](https://github.com/shraddha-an/time-series-gsr) : 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)](https://github.com/ZahraNabilaIzdihar/Backpropagation-Neural-Network-for-Multivariate-Time-Series-Forecasting) : NN

## Machine Learning for Univariate Input

* [Time Series Forecasting with the Long Short-Term Memory Network in Python](https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/) : LSTM
* [rishikksh20/LSTM-Time-Series-Analysis: Using LSTM network for time series forecasting](https://github.com/rishikksh20/LSTM-Time-Series-Analysis) Dataset: Sunspot Zurich, Model: LSTM
* [sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM: Time Series Forecasting using LSTM in Keras.](https://github.com/sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM) Dataset: Air Pollution, Model: LSTM
* [pushpendughosh/Stock-market-forecasting: Forecasting directional movements of stock prices for intraday trading using LSTM and random forest](https://github.com/pushpendughosh/Stock-market-forecasting) 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.](https://github.com/deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-) Dataset: Amazon Stock Model: LSTM with addition
* [demmojo/lstm-electric-load-forecast: Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network](https://github.com/demmojo/lstm-electric-load-forecast) 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.](https://github.com/Yifeng-He/Electric-Power-Hourly-Load-Forecasting-using-Recurrent-Neural-Networks) Dataset: Electricity Consumption Model: LSTM
* [Yongyao/enso-forcasting: Improving the forecasting accuracy of ENSO through deep learning](https://github.com/Yongyao/enso-forcasting) 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](https://github.com/EsmeYi/time-series-forcasting) Dataset: Sensor, Model: LSTM
* [CynthiaKoopman/Forecasting-Solar-Energy: Forecasting Solar Power: Analysis of using a LSTM Neural Network](https://github.com/CynthiaKoopman/Forecasting-Solar-Energy) Dataset: Solar power, Model: LSTM
* [3springs/attentive-neural-processes: implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)](https://github.com/3springs/attentive-neural-processes) 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.](https://github.com/Housiadas/forecasting-energy-consumption-LSTM), 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](https://github.com/pyaf/load_forecasting) 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](https://github.com/jiegzhan/time-series-forecasting-rnn-tensorflow) 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](https://github.com/zhangxu0307/time_series_forecasting_pytorch) 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](https://github.com/rakshita95/DeepLearning-time-series) Dataset: ?? Model: ARIMA, VAR, LSTM
* [mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM](https://github.com/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.](https://github.com/stxupengyu/load-forecasting-algorithms) 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.](https://github.com/Abhishekmamidi123/Time-Series-Forecasting) 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](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/)
* [How to Make Manual Predictions for ARIMA Models with Python](https://machinelearningmastery.com/make-manual-predictions-arima-models-python/)
* [How to Make Out-of-Sample Forecasts with ARIMA in Python](https://machinelearningmastery.com/make-sample-forecasts-arima-python/)
* [rakshita95/DeepLearning-time-series: LSTM for time series forecasting](https://github.com/rakshita95/DeepLearning-time-series)
* [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.](https://github.com/Abhishekmamidi123/Time-Series-Forecasting)
* [mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM](https://github.com/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](https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/)
* [rakshita95/DeepLearning-time-series: LSTM for time series forecasting](https://github.com/rakshita95/DeepLearning-time-series)
* [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.](https://github.com/Abhishekmamidi123/Time-Series-Forecasting)
* [mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM](https://github.com/mborysiak/Time-Series-Forecasting-with-ARIMA-and-LSTM)
* [Time Series Forecasting with the Long Short-Term Memory Network in Python](https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-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](https://en.wikipedia.org/wiki/Stationary_process))

* [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+](https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/) : VAR
* [shraddha-an/time-series-gsr: Multivariate Time-Series Forecasting of Gas Sensor Array Readings. Accompanying Medium article below.](https://github.com/shraddha-an/time-series-gsr) : 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

* [How to Develop LSTM Models for Time Series Forecasting](https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/)
* [Multi-Step LSTM Time Series Forecasting Models for Power Usage](https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/)
* [Multivariate Time Series Forecasting with LSTMs in Keras](https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/)
* [vb100/multivariate-lstm](https://github.com/vb100/multivariate-lstm)
* [Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM](https://github.com/Shiv-Kumar-Yadav9/Stock-Price-Prediction-by-Multivariate-Multistep-LSTM)
* [How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution](https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/)

## Prophet and Kats from Facebook

* [Time-Series Forecasting with Facebook Prophet and OmniSci](https://www.omnisci.com/blog/time-series-forecasting-with-facebook-prophet-and-omnisci?utm_source=reddit\&utm_medium=social\&utm_campaign=technical-content\&utm_term=time-series-forecasting-with-facebook-prophet-and-omnisci\&utm_content=blog)
* [Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy?](https://www.microprediction.com/blog/prophet), [HN Discussion](https://news.ycombinator.com/item?id=27695574)
* [Kats - Kats](https://facebookresearch.github.io/Kats/)
* [ourownstory/neural\_prophet: NeuralProphet: A simple forecasting package](https://github.com/ourownstory/neural_prophet)
* [NeuralProphet: The neural evolution of Meta’s Prophet](https://ai.facebook.com/blog/neuralprophet-the-neural-evolution-of-facebooks-prophet/)

## Note on Multivariate and Univariate

* [On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting](https://machinelearningmastery.com/suitability-long-short-term-memory-networks-time-series-forecasting/)
* [A Comparative Study between Univariate and Multivariate Linear Stationary Time Series Models](http://article.sapub.org/10.5923.j.ajms.20160605.02.html)
* [Alro10/deep-learning-time-series: List of papers, code and experiments using deep learning for time series forecasting](https://github.com/Alro10/deep-learning-time-series) Collection of papers

## Software

* [Gekko Timeseries and Modeling Software - Timeseries handling, and solving of large-scale economic models](http://t-t.dk/gekko/)

## 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](https://github.com/chlubba/catch22)
* [blue-yonder/tsfresh: Automatic extraction of relevant features from time series:](https://github.com/blue-yonder/tsfresh)

## Precipitation Forecasting

* Nowcasting, Upsampling, Interpolation, Super resolution
* [hydrogo/rainnet: RainNet: a convolutional neural network for radar-based precipitation nowcasting](https://github.com/hydrogo/rainnet)
* [1706.03458 Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://arxiv.org/abs/1706.03458)
  * [sxjscience/HKO-7: Source code of paper "NIPS2017 Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model"](https://github.com/sxjscience/HKO-7)

## Deep Learning for Forecasting

top open source deep learning for time series forecasting frameworks.

1. [Gluon](https://github.com/awslabs/gluon-ts) 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.
2. [Flow Forecast](https://github.com/AIStream-Peelout/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.
3. [sktime dl](https://github.com/sktime/sktime-dl) This is another time series forecasting repository. Unfortunately it looks like particularly recent activity has diminished on it.
4. [PyTorch-TS](https://github.com/zalandoresearch/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).

## eBook Forecasting

* [Forecasting: Principles and Practice (2nd ed)](https://otexts.com/fpp2/)
* [Forecasting: Principles and Practice (3rd ed)](https://otexts.com/fpp3/index.html)
* [Time Series Analysis and Its Applications: With R Examples - tsa4](https://www.stat.pitt.edu/stoffer/tsa4/)
  * [nickpoison/tsa4: R code for Time Series Analysis and Its Applications, Ed 4](https://github.com/nickpoison/tsa4)
* [Time Series: A Data Analysis Approach Using R](https://www.stat.pitt.edu/stoffer/tsda/)
* [NIST/SEMATECH e-Handbook of Statistical Methods](https://www.itl.nist.gov/div898/handbook/index.htm) engineering statistics

## Timeseries Forecasting

* [linkedin/greykite: A flexible, intuitive and fast forecasting library](https://github.com/linkedin/greykite)
* [alan-turing-institute/sktime: A unified framework for machine learning with time series](https://github.com/alan-turing-institute/sktime)
* [unit8co/darts: A python library for easy manipulation and forecasting of time series.](https://github.com/unit8co/darts)
* [Time Series Forecasting - Machine Learning - Amazon Forecast](https://aws.amazon.com/forecast/?nc1=h_ls)
* [Prophet - Forecasting at scale.](https://facebook.github.io/prophet/)

## Timeseries Forecasting Book

* [Forecasting: Principles and Practice (2nd ed)](https://otexts.com/fpp2/)
* [Introduction to Time Series and Forecasting - SpringerLink](https://link.springer.com/book/10.1007/978-3-319-29854-2)
* [Amazon.com: Practical Time Series Analysis: Prediction with Statistics and Machine Learning: 9781492041658: Nielsen, Aileen: Books](https://www.amazon.com/Practical-Time-Analysis-Prediction-Statistics/dp/1492041653/?language=en_US)
* [Amazon.com: An Introduction to High-Frequency Finance: 9780122796715: Gençay, Ramazan, Dacorogna, Michel, Muller, Ulrich A., Pictet, Olivier, Olsen, Richard: Books](https://www.amazon.com/Introduction-High-Frequency-Finance-Ramazan-Gen%C3%A7ay/dp/0122796713/?currency=USD\&language=en_US)

## Timeseries Forecasting Reading

* [Time Series Analysis and Forecasting with ARIMA - kanoki](https://kanoki.org/2020/04/30/time-series-analysis-and-forecasting-with-arima-python/)
* [Makridakis Competitions - Wikipedia](https://en.wikipedia.org/wiki/Makridakis_Competitions)
* [AileenNielsen/TimeSeriesAnalysisWithPython](https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython)
* [ARIMA Model - Complete Guide to Time Series Forecasting in Python - ML+](https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/)
* [Aileen Nielsen Time Series Analysis PyCon 2017 - YouTube](https://www.youtube.com/watch?v=d3PtB5z9MyE)
* [Time Series Analysis with Python Intermediate - SciPy 2016 Tutorial - Aileen Nielsen - YouTube](https://www.youtube.com/watch?v=JNfxr4BQrLk)
* [Sorry ARIMA, but I’m Going Bayesian - Stitch Fix Technology–Multithreaded](https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/)
* [11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/)
* [Hidden Markov Models - An Introduction - QuantStart](https://www.quantstart.com/articles/hidden-markov-models-an-introduction/)
* [Nixtla/nixtla: Automated time series processing and forecasting.](https://github.com/Nixtla/nixtla)
* [Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy?](https://www.microprediction.com/blog/prophet)
* [unit8co/darts: A python library for easy manipulation and forecasting of time series.](https://github.com/unit8co/darts)
* [Introduction—statsmodels](https://www.statsmodels.org/stable/index.html)
* [Benchmarking Facebook’s Prophet–Nikolaos Kourentzes](https://kourentzes.com/forecasting/2017/07/29/benchmarking-facebooks-prophet/)

## Timeseries RNN

* [Spice.ai](https://github.com/spiceai)

## Timeseries Forecasting

* [Forecasting: Principles and Practice (3rd ed)](https://otexts.com/fpp3/)
* [Principles of Econometrics with R](https://bookdown.org/ccolonescu/RPoE4/)
* [Introduction to Econometrics with R](https://www.econometrics-with-r.org/index.html)
* [Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How - Databricks](https://databricks.com/session/time-series-forecasting-using-recurrent-neural-network-and-vector-autoregressive-model-when-and-how)
* [Economics 312 Reading List - Economics - Reed College](https://www.reed.edu/economics/parker/s10/312/readings.html)

## Time-series Forecasting

* [Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.](https://github.com/Nixtla/statsforecast)
* [statsforecast - Statistical ⚡️ Forecast](https://nixtla.github.io/statsforecast/)

## VAR

* [Vector Autoregression (VAR) - Comprehensive Guide with Examples in Python - Machine Learning Plus](https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/)
* [Time-series Analysis with VAR & VECM: Statistical approach - by Sarit Maitra - Towards Data Science](https://towardsdatascience.com/vector-autoregressions-vector-error-correction-multivariate-model-a69daf6ab618)
* [Vector Auto-regression Time series Model - by seemakurthi teja - Medium](https://medium.com/@seemakurthi.teja.1999/vector-auto-regression-time-series-model-d7ed5cb943f2)
* [Vector autoregression models](https://kevinkotze.github.io/ts-7-var/)
* [A vector autoregression weather model for electricity supply and demand modeling - SpringerLink](https://link.springer.com/article/10.1007/s40565-017-0365-1#Tab5)

## time Series

* [Time-Series Analysis Course](https://www.intel.com/content/www/us/en/developer/learn/course-time-series-analysis.html)
* [Online DSS: Time Series Analysis for Business Forecasting with Python - Algoritma](https://algorit.ma/ds-course/forecasting-python/#toggle-id-5-closed)
* [Time Series Analysis (and Forecasting) - Free Statistics and Forecasting Software (Calculators) v.1.2.1](http://www.wessa.net/tsa.wasp)
* [Time Series - solver](https://www.solver.com/time-series)
* [Time Series Lab - Advanced Time Series Forecasting Software](https://timeserieslab.com/)
* [NCAR Command Language (NCL)](https://www.ncl.ucar.edu/index.shtml)

## LSTM

* [LSTM on Pytorch](https://cnvrg.io/pytorch-lstm/)

## Time Series Toolbox

* Prophet [Prophet | Forecasting at scale.](https://facebook.github.io/prophet/)
* [Forecasting Models for Tidy Time Series • fable](https://fable.tidyverts.org/) in R
* [unit8co/darts: A python library for easy manipulation and forecasting of time series.](https://github.com/unit8co/darts) in Python
* [CRAN - Package forecast](https://cran.r-project.org/web/packages/forecast/index.html) in R [robjhyndman/forecast: forecast package for R](https://github.com/robjhyndman/forecast)
* 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](https://tsfresh.readthedocs.io/en/latest/)
* [MiniRocket—sktime documentation](https://www.sktime.org/en/v0.8.2/api_reference/auto_generated/sktime.transformations.panel.rocket.MiniRocket.html)
* 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—pytorch-forecasting documentation](https://pytorch-forecasting.readthedocs.io/en/stable/tutorials/stallion.html)
* sktime
* bssts
* statsmodels
* [https://github.com/fraunhoferportugal/tsfel](https://github.com/fraunhoferportugal/tsfel)

## Books

* [Forecasting: Principles and Practice (2nd ed)](https://otexts.com/fpp2/)

## Forecasting Comparison

* [statsforecast/experiments/m3 at main · Nixtla/statsforecast](https://github.com/Nixtla/statsforecast/tree/main/experiments/m3) # Statistical vs Deep Learning forecasting methods
* [Nixtla/statsforecast: Lightning ⚡️ fast forecasting with statistical and econometric models.](https://github.com/Nixtla/statsforecast)
