?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For cooperation on MATLAB projects, please send a private message.
Personal homepage: Matlab Research Studio
Personal credo: Investigate things to gain knowledge.
For more complete Matlab code and simulation customization content, click
Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
Time series forecasting is an important data analysis technique that can be used to predict future trends and changes. Temporal convolutional neural network (TCN) is an emerging neural network structure that performs well in time series prediction. This article will introduce the algorithm steps to implement data regression prediction based on TCN.
- data preparation
First, we need to prepare the data for training and testing the model. The data should be a time series with a corresponding value for each time point. We can use historical data to train a model and then use the model to predict future values.
- Data preprocessing
Before inputting data into the model, we need to preprocess it. This includes normalizing and scaling the data to an appropriate range so that the model can better learn the characteristics of the data. We can also use techniques such as differencing and smoothing to further reduce noise in the data.
- Build model
Next, we need to build the TCN model. TCN is a convolutional neural network with depth and width that can handle time series of different lengths. When building the model, we need to specify some parameters such as convolution kernel size, number of filters, and pooling size. The choice of these parameters will affect the performance of the model.
- Training model
Once we have built the model, we can use the training data to train it. During the training process, we need to define the loss function and optimizer so that the model can learn the characteristics of the data and reduce errors. We can use techniques such as cross-validation and regularization to further improve the performance of the model.
- Model evaluation
After training the model, we need to evaluate it. We can use the test data to evaluate the performance of the model, such as calculating the mean square error and mean absolute error. We can also use visualization techniques, such as graphing predicted versus actual results, to help us understand the model’s performance.
- Model optimization
If we find that the performance of the model is not good enough, we can use some techniques to further optimize it. This includes adjusting the parameters of the model, adding regularization terms, and using more complex model structures. We can also use techniques such as ensemble learning and transfer learning to further improve the performance of the model.
- Model application
Finally, we can use the trained model to make predictions. We can apply models to new data sets to predict future trends and changes. We can also use techniques such as time series decomposition and periodic analysis to further analyze the data and extract useful information.
Summarize
This article introduces the algorithm steps to implement data regression prediction based on TCN. This method excels in time series forecasting and can be used to predict future trends and changes. By carefully tuning the model’s parameters and using some techniques, we can further improve the model’s performance. This method can be applied to many fields, such as finance, meteorology and transportation.
Part of the code
%% Clear environment variables
warning off % Turn off alarm information
close all % Close the open figure window
clear % clear variables
clc % clears the command line
%% Import Data
res = xlsread('dataset.xlsx');
%% Divide training set and test set
temp = randperm(357);
P_train = res(temp(1: 240), 1: 12)';
T_train = res(temp(1: 240), 13)';
M = size(P_train, 2);
P_test = res(temp(241: end), 1: 12)';
T_test = res(temp(241: end), 13)';
N = size(P_test, 2);
%% Data normalization
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);
t_train = ind2vec(T_train);
t_test = ind2vec(T_test );
Run results
Edit
References
[1] Ding Wei. Research on short-term forecasting of wind power power based on temporal convolutional neural network[J].[2023-10-26].