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⛄ 内容介绍
一种基于LSTM模型的股票预测方法和系统,属于股票预测技术领域.本发明技术方法通过搭建深度学习环境,爬取大型企业近期的股票数据,对股票数据进行前期分析,再提取关键特征,选取训练数据,输入训练数据,基于深度学习理论构建股票预测模型,所述股票预测模型包括一层输入层,一层隐含层和一层输出层,最后输出预测结果,结合真实值以误差百分比作为评测指标进行测评.
⛄ 部分代码
while t<Max_iter
t
for i=1:size(Positions,1)
% Return back the search agents that go beyond the boundaries of the search space
Flag4ub=Positions(i,:)>ub;
Flag4lb=Positions(i,:)<lb;
Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
% Calculate objective function for each search agen
gam=Positions(i,1);
sig2=Positions(i,2);
model=initlssvm(train_x,train_yy,type,gam,sig2,kernel,proprecess);
model=trainlssvm(model);
%求出训练集和测试集的预测值
[train_predict_y,zt,model]=simlssvm(model,train_x);
[test_predict_y,zt,model]=simlssvm(model,test_x);
%预测数据反归一化
train_predict=postmnmx(train_predict_y ,miny,maxy);%预测输出
test_predict=postmnmx(test_predict_y ,miny,maxy); %测试集预测值
%计算均方差
trainmse=sum((train_predict-train_y).^2)/length(train_y);
testmse=sum((test_predict-test_y).^2)/length(test_y);
fitness=trainmse; %以测试集的预测值计算的均方差为适应度值
% Update the leader
if fitness<Leader_score % Change this to > for maximization problem
Leader_score=fitness; % Update alpha
Leader_pos=Positions(i,:);%最佳参数
YPred_best=test_predict;
end
end
a=-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)
% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
a2=t*((-1)/Max_iter);
% Update the Position of search agents
for i=1:size(Positions,1)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
A=2*a*r1-a; % Eq. (2.3) in the paper
C=2*r2; % Eq. (2.4) in the paper
b=1; % parameters in Eq. (2.5)
l=(a2-1)*rand+1; % parameters in Eq. (2.5)
p = rand(); % p in Eq. (2.6)
for j=1:size(Positions,2)
if p<0.5
if abs(A)>=1
rand_leader_index = floor(SearchAgents_no*rand()+1);
X_rand = Positions(rand_leader_index, :);
D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)
Positions(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8)
elseif abs(A)<1
D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)
Positions(i,j)=Leader_pos(j)-A*D_Leader; % Eq. (2.2)
end
elseif p>=0.5
distance2Leader=abs(Leader_pos(j)-Positions(i,j));
% Eq. (2.5)
Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);
end
end
end
t=t+1;
Convergence_curve(t)=Leader_score;
% [t Leader_score]
end
⛄ 运行结果
⛄ 参考文献
[1]郝可青, 吕志刚, 邸若海,等. 基于鲸鱼算法优化长短时记忆神经网络的锂电池剩余寿命预测[J]. 科学技术与工程, 2022, 22(29):9.
[2]彭 燕,刘宇红,张荣芬. 基于LSTM的股票价格预测建模与分析[J]. 计算机工程与应用(209-212).
