How can we improve multi step prediction performance

Certainly! Improving the performance of multi-step prediction in MATLAB involves using appropriate techniques and models. Here are some strategies:

1. Dynamic Neural Networks:

   • Consider using dynamic networks with feedback, such as narxnet and narnet neural networks.
   • Transform these networks between open-loop and closed-loop modes using openloop and closeloop functions.
   • Closed-loop networks make multistep predictions by using internal feedback even when external feedback is missing¹.

2. Linear Regression:

   • For low to medium-dimensional data sets, use linear regression models with fitlm for greater accuracy.
   • For high-dimensional data sets, opt for fitrlinear to reduce computation time².

3. LSTM (Long Short-Term Memory):

   • When dealing with time-series data, LSTM networks are effective for multistep forecasting.
   • Divide your data into training and test sets.
   • Use a proportion (e.g., 90%) for training and the remaining for testing.
   • Prepare the data and train the LSTM model to predict multiple steps ahead³⁴.