在MATLAB中写出对时间序列小波变换利用mallat算法进行分解，将分解后的小波系数进行自适应阈值去噪，将去噪后的小波系数按照自回归和最小二乘支持向量机进行时间序列预测，最后将预测结果按照mallat算法进行重构的代码

我可以回答这个问题。以下是MATLAB中对时间序列小波变换利用mallat算法进行分解，将分解后的小波系数进行自适应阈值去噪，将去噪后的小波系数按照自回归和最小二乘支持向量机进行时间序列预测，最后将预测结果按照mallat算法进行重构的代码：

% Load time series data
load('time_series_data.mat');

% Perform Mallat wavelet decomposition
[c,l] = wavedec(data,3,'db4');

% Set threshold for wavelet coefficients
thr = wthrmngr('dw2ddenoLVL','penalhi',c,l,3);

% Perform soft thresholding on wavelet coefficients
s = wthresh(c,'s',thr);

% Reconstruct denoised time series
denoised_data = waverec(s,l,'db4');

% Split data into training and testing sets
train_data = denoised_data(1:800);
test_data = denoised_data(801:end);

% Perform autoregression on training data
ar_model = ar(train_data,10);

% Predict future values using autoregression model
predicted_data_ar = predict(ar_model,test_data);

% Perform least squares support vector machine on training data
svm_model = fitrsvm(train_data(1:end-1),train_data(2:end),'KernelFunction','linear');

% Predict future values using support vector machine model
predicted_data_svm = predict(svm_model,test_data(1:end-1));

% Combine predicted data from both models
predicted_data = [train_data(end); predicted_data_ar; predicted_data_svm];

% Reconstruct predicted time series using Mallat wavelet reconstruction
predicted_data = waverec(wavedec(predicted_data,3,'db4'),l,'db4');