Multidimensional time series | MATLAB implements CNN-BiGRU convolutional neural network combined with bidirectional gated recurrent unit multivariate time series prediction

Multidimensional time series | MATLAB implements CNN-BiGRU convolutional neural network combined with bidirectional gated recurrent unit multivariate time series prediction

forecast result

basic introduction

Multi-dimensional time series | MATLAB implements CNN-BiGRU convolutional neural network combined with bidirectional gated recurrent unit multivariate time series prediction
1. Matlab implements CNN-BiGRU convolutional neural network combined with bidirectional gated recurrent unit multivariate time series prediction;
2. Operating environment For Matlab2020b;
3. Input multiple features, output a single variable, consider the influence of historical features, multivariate time series prediction;
4.data is a data set, MainCNN_BiGRUNTS.m is the main program, just run it, and put all files in one file 5.
The command window outputs R2, MSE, MAE, MAPE and MBE multi-index evaluation;

Model Features

The CNN-BiGRU model combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU), and is mainly used for forecasting multivariate time series data. The main features of the model are summarized below:

• Modeling the correlation between multiple variables: The CNN-BiGRU model is able to model the correlation between multiple variables, thereby improving the accuracy and stability of predictions.
• Adaptive feature selection: The model is able to adaptively select important features, thereby reducing the impact of noise and redundant information on prediction results.
• Sequence modeling capability: Bidirectional gated recurrent units are able to learn long-term dependencies in sequences, which can better capture dynamic changes in time series.
• Robustness: The CNN-BiGRU model can handle missing values ​​and outliers, and the input data is normalized and standardized, which improves the robustness and generalization ability of the model.
• Scalability: The model can flexibly adjust the network structure and hyperparameters to adapt to different data sets and tasks, and can be integrated and combined with other deep learning models to further improve the prediction effect.

It should be noted that the CNN-BiGRU model also has some limitations and challenges, such as long training time, complex model structure, large amount of data and computing resources, and so on. In practical applications, it is necessary to optimize and improve the model according to the specific situation to improve the prediction effect and efficiency.

programming

• The complete source code and data can be obtained by private message bloggers.

% 定义CNN层参数
filterSize = [3 3];
numFilters = 32;
poolSize = [2 2];

% 定义BiGRU层参数
numHiddenUnits = 64;
outputMode = 'last';

% 定义全连接层参数
numClasses = 1;

% 定义网络结构
layers = [
    sequenceInputLayer(numFeatures)
    convolution2dLayer(filterSize, numFilters, 'Padding', 1)
    reluLayer
    maxPooling2dLayer(poolSize, 'Stride', 2)
    sequenceFoldingLayer
    gruLayer(numHiddenUnits, 'OutputMode', outputMode, 'Bidirectional', true)
    fullyConnectedLayer(numClasses)
    regressionLayer];

% 定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 32, ...
    'ValidationData', {
    
    XValidation, YValidation}, ...
    'ValidationFrequency', 10, ...
    'Verbose', false);

% 训练模型
net = trainNetwork(XTrain, YTrain, layers, options);

% 预测结果
YPred = predict(net, XTest);

summarize

The CNN-BiGRU model combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU), and is mainly used for forecasting multivariate time series data. The main features of this model are the ability to model the correlation among multiple variables and the ability to adaptively select important features.
Specifically, the model first uses a convolutional neural network to extract features from time-series data of multiple variables, and then inputs these features into a bidirectional gated recurrent unit for sequence modeling. Bidirectionally gated recurrent units are able to learn long-term dependencies in sequences and can adaptively select which features are most useful for predicting outcomes. Finally, the model uses a fully connected layer to process the output to get the final prediction.
To use this model for multivariate time series forecasting, it is necessary to prepare the corresponding data set, and to train and adjust the parameters of the model. In practical applications, it is also necessary to optimize and improve the model according to the specific situation to improve the prediction effect.

References

[1] https://blog.csdn.net/kjm13182345320/article/details/127515229?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/127993418?spm=1001.2014.3001.5502