Multi-input single-output prediction: Matlab implementation combining multiple regression analysis and LSTM neural network - Code World

Multi-input single-output prediction: Matlab implementation combining multiple regression analysis and LSTM neural network

Enterprise 2023-09-16 02:40:48 views: null

Multi-input single-output prediction: Matlab implementation combining multiple regression analysis and LSTM neural network

In this article, we will introduce how to use multiple regression analysis and LSTM (long short-term memory) neural network to make multi-input and single-output predictions. We will use Matlab as the programming tool and provide the corresponding source code.

Multiple regression analysis is a commonly used statistical analysis method used to study the relationship between multiple independent variables and a dependent variable. The LSTM neural network is a recursive neural network that is particularly suitable for processing data with time series characteristics. By combining these two methods, we are better able to leverage time series data and other relevant features for predictive analysis.

The following is our Matlab code that combines multiple regression analysis with LSTM neural network:

% 导入数据
data = csvread('input_data.csv'); % 替换为实际数据文件路径

% 划分训练集和测试集
train_ratio = 0.8; % 训练集比例

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