version 1.0 (4.89 MB) by Takashi
This page shows how to implement time series models and to update them and forecast value at next time step recursively.


Updated 20 May 2020

From GitHub

View License on GitHub

Please click the following URL, if you prefer to Japanese.

This example set introduce how to implement arbitrary time series models on the Simulink concretely if you don't need code generation.

Each folder has MATLAB codes and a Simulink model, and their names correspond to time series models or layers of neural network respectively.
This page focuses on the 2 products.​

* Deep Learning Toolbox™​
* Econometrics Toolbox™​

They offer features to forecast time series recursively and each example describes how to implement their features on the Simulink and to invoke them via the MATLAB Function block. However this technique does not apply only to the above products but can be adopted additional features for time series analysis in particular regression, which are provided by other products such as

- Predictive Maintenance Toolbox™​
- Statistics and Machine Learning Toolbox™​
- System Identification Toolbox™​

Cite As

Takashi (2022). Time-Series-Forecasting-Simulink (https://github.com/mathworks/Time-Series-Forecasting-Simulink/releases/tag/v1.0), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.