Time series prediction | Matlab implements CNN-XGBoost convolutional neural network combined with extreme gradient boosting tree time series prediction

Time series prediction | Matlab implements CNN-XGBoost convolutional neural network combined with extreme gradient boosting tree time series prediction

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Basic description

Time series prediction | Matlab implements CNN-XGBoost convolutional neural network combined with extreme gradient boosting tree time series prediction.
Matlab implements CNN-XGBoost convolutional neural network combined with extreme gradient boosting tree time series prediction (complete source code and data)
1. data is a data set, a univariate time series data set.
2. CNN_XGBoostTS.m is the main program file, and the others are function files, which do not need to be run;
3. Evaluation indicators R2, MAE, MAPE, MSE, RMSE;
4. Note that the program and data are placed in a folder, and the folder cannot be named XGBoost , because some functions have been used, and the operating environment is Matlab2020 and above.

programming

• Complete program and data download: Matlab implements CNN-XGBoost convolutional neural network combined with extreme gradient boosting tree time series prediction .

Function_name='F1'; % Name of the test function that can be 
end
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  设置必要的指针
h_test_ptr = libpointer;
h_test_ptr_ptr = libpointer('voidPtrPtr', h_test_ptr);
test_ptr = libpointer('singlePtr', single(p_test));
calllib('xgboost', 'XGDMatrixCreateFromMat', test_ptr, rows, cols, model.missing, h_test_ptr_ptr);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  预测
out_len_ptr = libpointer('uint64Ptr', uint64(0));
f = libpointer('singlePtr');
f_ptr = libpointer('singlePtrPtr', f);
calllib('xgboost', 'XGBoosterPredict', h_booster_ptr, h_test_ptr, int32(0), uint32(0), int32(0), out_len_ptr, f_ptr);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  提取预测
n_outputs = out_len_ptr.Value;
setdatatype(f, 'singlePtr', n_outputs);
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原文链接：https://blog.csdn.net/kjm13182345320/article/details/124929272

References

[1] https://blog.csdn.net/kjm13182345320/article/details/127596777?spm=1001.2014.3001.5501
[2] https://download.csdn.net/download/kjm13182345320/86830096?spm=1001.2014.3001.5501