Time series prediction | Matlab implements EEMD-SSA-BiLSTM, EEMD-BiLSTM, SSA-BiLSTM, BiLSTM time series prediction comparison

Time series prediction | Matlab implements EEMD-SSA-BiLSTM, EEMD-BiLSTM, SSA-BiLSTM, and BiLSTM time series prediction comparison

Prediction effect

basic introduction

1.Matlab implements EEMD-SSA-BiLSTM, EEMD-BiLSTM, SSA-BiLSTM and BiLSTM time series prediction comparison, ensemble empirical mode decomposition combined with Sparrow algorithm to optimize bidirectional long short-term memory neural network, ensemble empirical mode decomposition combined with bidirectional long short-term memory Neural network, Sparrow algorithm optimization bidirectional long short-term memory neural network, bidirectional long short-term memory neural network time series prediction comparison.
2.EEMD-SSA-BiLSTM is a time series forecasting method based on ensemble empirical mode decomposition (EEMD), Sparrow algorithm (SSA) and bidirectional long short-term memory neural network (BiLSTM);
first, use the EEMD method to predict the original time series Decompose and obtain multiple intrinsic mode functions (IMF). Then, the SSA algorithm is used to optimize each IMF to obtain the optimal model parameters. Finally, all IMF forecast results are added to obtain the final forecast result. The advantage of the EEMD-SSA-BiLSTM method is that it can fully mine the nonlinear and non-stationary characteristics of the time series, and can adaptively optimize each IMF, improving the accuracy and robustness of the prediction, and can be applied to various fields. A variety of time series prediction problems, such as stock price prediction, weather data prediction, traffic flow prediction, etc.
3. The operating environment is Matlab2018b and above, just run the main of each subfolder, and the excel data is easy to replace;

programming

• Complete program and data download method: Private message the blogger to reply to Matlab to implement EEMD-SSA-BiLSTM, EEMD-BiLSTM, SSA-BiLSTM, and BiLSTM timing prediction comparison .

%% 采用ssa优化
[x ,fit_gen,process]=ssaforlstm(XTrain,YTrain,XTest,YTest);%分别对隐含层节点 训练次数与学习率寻优
%% 参数设置
pop=5; % 种群数
M=20; % 最大迭代次数
%初始化种群
for i = 1 : pop
    for j=1:dim
        if j==1%除了学习率 其他的都是整数
            x( i, j ) = (ub(j)-lb(j))*rand+lb(j);
        else
            x( i, j ) = round((ub(j)-lb(j))*rand+lb(j));
        end
    end
    fit( i )=fitness(x(i,:),P_train,T_train,P_test,T_test);
end
pFit = fit;
pX = x;
fMin=fit(1);
bestX = x( i, : );

for t = 1 : M
    
    [ ~, sortIndex ] = sort( pFit );% Sort.从小到大
    [fmax,B]=max( pFit );
    worse= x(B,:);
    r2=rand(1);
    %%%%%%%%%%%%%5%%%%%%这一部位为发现者（探索者）的位置更新%%%%%%%%%%%%%%%%%%%%%%%%%
    if(r2<0.8)%预警值较小，说明没有捕食者出现
        for i = 1 : pNum  %r2小于0.8的发现者的改变（1-20）                                                 % Equation (3)
            r1=rand(1);
            x( sortIndex( i ), : ) = pX( sortIndex( i ), : )*exp(-(i)/(r1*M));%对自变量做一个随机变换
            x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );%对超过边界的变量进行去除
            
            fit(  sortIndex( i ) )=fitness(x(sortIndex( i ),:),P_train,T_train,P_test,T_test);
        end
    else   %预警值较大，说明有捕食者出现威胁到了种群的安全，需要去其它地方觅食
        for i = 1 : pNum   %r2大于0.8的发现者的改变
            x( sortIndex( i ), : ) = pX( sortIndex( i ), : )+randn(1)*ones(1,dim);
            x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
            
            fit(  sortIndex( i ) )=fitness(x(sortIndex( i ),:),P_train,T_train,P_test,T_test);
            
        end
        
    end
    [ ~, bestII ] = min( fit );
    bestXX = x( bestII, : );
    %%%%%%%%%%%%%5%%%%%%这一部位为加入者（追随者）的位置更新%%%%%%%%%%%%%%%%%%%%%%%%%
    for i = ( pNum + 1 ) : pop     %剩下20-100的个体的变换                % Equation (4)
        %         i
        %         sortIndex( i )
        A=floor(rand(1,dim)*2)*2-1;
        if( i>(pop/2))%这个代表这部分麻雀处于十分饥饿的状态（因为它们的能量很低，也是是适应度值很差），需要到其它地方觅食
            x( sortIndex(i ), : )=randn(1,dim).*exp((worse-pX( sortIndex( i ), : ))/(i)^2);
        else%这一部分追随者是围绕最好的发现者周围进行觅食，其间也有可能发生食物的争夺，使其自己变成生产者
            x( sortIndex( i ), : )=bestXX+(abs(( pX( sortIndex( i ), : )-bestXX)))*(A'*(A*A')^(-1))*ones(1,dim);
        end
        x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );%判断边界是否超出
        fit(  sortIndex( i ) )=fitness(x(sortIndex( i ),:),P_train,T_train,P_test,T_test);
    end
    %%%%%%%%%%%%%5%%%%%%这一部位为意识到危险（注意这里只是意识到了危险，不代表出现了真正的捕食者）的麻雀的位置更新%%%%%%%%%%%%%%%%%%%%%%%%%
    c=randperm(numel(sortIndex));%%%%%%%%%这个的作用是在种群中随机产生其位置（也就是这部分的麻雀位置一开始是随机的，意识到危险了要进行位置移动，
    %处于种群外围的麻雀向安全区域靠拢，处在种群中心的麻雀则随机行走以靠近别的麻雀）
    b=sortIndex(c(1:pop));
    for j =  1  : length(b)      % Equation (5)
        if( pFit( sortIndex( b(j) ) )>(fMin) ) %处于种群外围的麻雀的位置改变
            x( sortIndex( b(j) ), : )=bestX+(randn(1,dim)).*(abs(( pX( sortIndex( b(j) ), : ) -bestX)));
        else
            %处于种群中心的麻雀的位置改变
            x( sortIndex( b(j) ), : ) =pX( sortIndex( b(j) ), : )+(2*rand(1)-1)*(abs(pX( sortIndex( b(j) ), : )-worse))/ ( pFit( sortIndex( b(j) ) )-fmax+1e-50);
        end
        x( sortIndex(b(j) ), : ) = Bounds( x( sortIndex(b(j) ), : ), lb, ub );
        fit(  sortIndex( b(j)  ) )=fitness(x(sortIndex( b(j) ),:),P_train,T_train,P_test,T_test);
        
    end

References

[1] https://blog.csdn.net/article/details/126072792?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/article/details/126044265?spm=1001.2014.3001.5502
[3] https://blog.csdn.net/article/details/126043107?spm=1001.2014.3001.5502