?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
Abstract: Time series forecasting is a key problem in many fields, including finance, weather forecasting, stock markets, etc. Convolutional neural networks (CNN) have achieved remarkable success in fields such as image processing and natural language processing, however, their applications in time series prediction are still relatively rare. This paper proposes a time series prediction algorithm based on convolutional neural network, which converts time series data into images and uses CNN models for prediction. This article details the steps of the algorithm and verifies its effectiveness through experiments.
-
Introduction Time series forecasting refers to predicting future values based on past observations. Time series forecasting is an important task in many fields. Traditional time series forecasting methods are usually based on statistical models, such as ARIMA, GARCH, etc. However, these methods often require multiple assumptions about the data and have weak ability to model nonlinear relationships in the data. Convolutional neural network (CNN), as a powerful nonlinear model, has achieved remarkable success in fields such as image processing and natural language processing. Therefore, applying CNN to time series prediction is a promising approach.
-
Algorithm Steps The time series prediction algorithm based on convolutional neural network proposed in this article mainly includes the following steps:
2.1 Data preprocessing First, the time series data needs to be preprocessed. Common preprocessing methods include removing noise, filling missing values, normalization, etc. For time series data, it is usually necessary to convert it into an image format to facilitate processing by the CNN model. Common conversion methods include encoding of time series to images, spectrogram conversion, etc.
2.2 Constructing a CNN model After data preprocessing, a convolutional neural network model needs to be constructed. The CNN model includes convolutional layers, pooling layers and fully connected layers. The convolutional layer is used to extract local features of time series data, the pooling layer is used to reduce the data dimension, and the fully connected layer is used to map the extracted features to predicted results. Depending on the actual situation, different network structures and parameter settings can be selected.
2.3 Model training and optimization After building the CNN model, the model needs to be trained and optimized. During the training process, common optimization algorithms can be used, such as stochastic gradient descent (SGD), Adam, etc. At the same time, an appropriate loss function also needs to be selected to measure the prediction error of the model. During the training process, methods such as cross-validation can be used to evaluate the performance of the model.
2.4 Time series prediction After model training and optimization, the trained CNN model can be used for time series prediction. During the prediction process, the input time series data is converted into image format and predicted through the CNN model. The prediction results can be subjected to subsequent processing according to actual needs, such as denormalization, post-processing, etc.
Experimental results In order to verify the effectiveness of the time series prediction algorithm based on convolutional neural network proposed in this article, we conducted a series of experiments. Experimental results show that the algorithm achieves good prediction results on different time series data sets. Compared with traditional time series forecasting methods, algorithms based on convolutional neural networks have stronger nonlinear modeling capabilities and better forecasting accuracy.
Conclusion This article proposes a time series prediction algorithm based on convolutional neural network and details the steps of the algorithm. Through experimental verification, the effectiveness and advantages of this algorithm in time series prediction tasks are proved. Future research can further explore how to further improve this algorithm to adapt to more complex time series forecasting problems.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Wu Junjie, Luo Yu, Liu Liang, et al. A time series load forecasting method based on convolutional neural network: CN202210198854.X[P].CN202210198854.X[2023-10-21].
Some theories are quoted from online literature. If there is any infringement, please contact the blogger to delete it
Follow me to receive massive matlab e-books and mathematical modeling materials
Private message complete code, paper reproduction, journal cooperation, paper tutoring and scientific research simulation customization
1 Improvement and application of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, electromyographic signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
The knowledge points of the article match the official knowledge files, and you can further learn relevant knowledge. Python entry skill treeHomepageOverview 383,494 people are learning the system