Time series forecasting based on GMDH algorithm with matlab code

Time series forecasting based on GMDH algorithm with matlab code

Time series forecasting is a special forecasting method, which explores its laws through statistical analysis and forecasting of time series data. Time series forecasting has become an important tool in data mining, financial forecasting, weather forecasting and other fields. As an effective forecasting algorithm, the GMDH algorithm has also been widely used in time series forecasting.

The GMDH (Group Method of Data Handling) algorithm is a data-driven modeling method, which uses a polynomial approximation method that continuously adds new dimensions to gradually improve the accuracy of the model. One advantage of the GMDH algorithm is that it can automatically select features and the training process is relatively fast.

Below we will introduce how to use the GMDH algorithm to implement time series forecasting, and provide the corresponding matlab code.

1. data preparation

First, we need to prepare a set of time series data. We use the volatility dataset here, which contains the volatility of the daily closing price of the S&P 500 index from 2000 to 2017.

In matlab, we can use the readtable function to read data files in csv format, and use the datetime function to convert date data into time format.

data = readtable('vix.csv');
dates = datetime