This page shows how to implement time series models and to update them and forecast value at next time step recursively.
https://github.com/mathworks/Time-Series-Forecasting-Simulink
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https://github.com/mathworks/Time-Series-Forecasting-Simulink
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 (2026). Time-Series-Forecasting-Simulink (https://github.com/mathworks/Time-Series-Forecasting-Simulink/releases/tag/v1.0), GitHub. Retrieved .
General Information
- Version 1.0 (5.08 MB)
-
View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0 |
