Time Series Prediction with Bayesian optimization

Time-series Prediction by LSTM and Bayesian Optimization algorithm for hyperparameter tuning in the univariate and multivariate dataset

Hope you are doing well,

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm.

in this work a bayesian optimization algorithm used for tuning the parameters of an LSTM in order to use for time series prediction.

----------------------|| Univariate Time Series Prediction ||---------------

Univariate Time Series refers to time series that consists of single (scalar) observations samples sequentially over time increments.

In this code, a Bayesian optimization algorithm is responsible for finding the optimal LSTM network values.

Covid 19 dataset.

https://youtu.be/5KZwQ6K2wMM

you can download the code from the link below:

https://dataanalysisresearch.com/product/univariatetimeseriespredictionbayesianoptimization/

.

Univariate Time Series Prediction with a Hybrid C-LSTM (Hybrid Convolutional Neural Network with LSTM) Model

for Bitcoin dataset.

https://www.youtube.com/watch?v=sNfPxFljfMc

----------------------|| Multivariate Time Series Prediction ||---------------

A Multivariate time series has more than one time-dependent variable and one sequential. Each variable depends not only on its past values but also has some dependency on other variables.

it could be in the form of

-Multivariable input and one output.

-Multivariable input and multivariable output.

In this code, a Bayesian optimization algorithm is responsible for finding the optimal LSTM network values.

Air quality dataset, part one.

https://www.youtube.com/watch?v=U-7_Jf6YdUA

you can download the code from the link below:

https://dataanalysisresearch.com/product/multivariatetimeseriespredictionbayesianoptimization/

.

If you have questions or would like to improve the code, don't hesitate

to mail me: [e-mail]

best wishes,

Abolfazl Nejatian