💥1 概述
宏观经济时间序列的分析和预测是国家决策者非常感兴趣的因素。然而，由于缺乏精确的经济模型和外部因素（如天气变化或政治决策）的影响，经济分析和预测不是简单的任务。我们的研究重点是西班牙语国家。在本论文中，我们研究了不同类型的神经网络及其在各种分析任务中的适用性，包括GDP预测以及评估各国发展的主要趋势。研究的模型包括多层神经网络、递归神经网络和Kohonen映射。分析了1980-2015年期间17个西班牙语国家以及法国和德国的历史宏观经济数据。这项工作随后比较了用于训练神经网络的各种算法的性能，并展示了各国经济状况的变化。此外，我们还提供了解释数据中发现的趋势的可能原因。
📚2 运行结果
🎉3 参考文献
[1]刘涛雄,徐晓飞.大数据与宏观经济分析研究综述[J].国外理论动态,2015(01):57-64.
👨💻4 Matlab代码
主函数部分代码：
%% Generating data points x = linspace(-2*pi, 2*pi, 500); y = sin(x); xextended = linspace(-4*pi,4*pi,1000); yextended = sin(xextended); %% Create and configure the MLP layer = [50 10]; % gradient descend (backpropagation) algorithm will be used net = feedforwardnet(layer,'traingd'); net = configure(net,x,y); net = init(net); for h = 1:length(layer) %Using sigmoid transfer function net.layers{h}.transferFcn = 'tansig'; end net.trainParam.max_fail = 150; net.trainParam.time = 600; net.trainParam.epochs = 1000; net.trainParam.goal = 1e-6; net.trainParam.lr = 0.01; %% Train the MLP [net,tr] = train(net,x,y); %% Generate the output of the MLP outputs = net(x); outputsextended = net(xextended); %% Create and configure the RNN % The RNN will use an input sequence of length 100 % gradient descend (backpropagation) algorithm will be used rnn = layrecnet(1:100,[15],'traingd'); rnn.trainParam.epochs = 2000; rnn.trainParam.lr = 0.01; %% Transforming the training data to fit the RNN [Xs,Xi,Ai,Ts] = preparets(rnn,num2cell(x),num2cell(y)); %% Training the RNN rnn = train(rnn,Xs,Ts,Xi,Ai); %% Transforming the testing data to fit the RNN [Xs2,Xi2,Ai2,Ts2] = preparets(rnn,num2cell(xextended),num2cell(yextended)); %% Generate the output of the RNN rnnoutput = rnn(Xs2,Xi2,Ai2); %% Transform the output of the RNN rnnoutput = cell2mat(rnnoutput); %% Generate the plot for the MLP figure plot(xextended(101:(length(xextended))),yextended(101:(length(yextended))),'Color',[1 0 0], 'LineWidth',2) hold on plot(xextended(101:(length(xextended))),outputsextended(101:length(outputsextended)),'Color', [0 1 0], 'LineWidth',2); line([-2*pi -2*pi], [-2 2], 'Color', [0 0 0]); line([2*pi 2*pi], [-2 2], 'Color', [0 0 0]); xlabel('-4\pi < x < 4\pi'); % x-axis label ylabel('sine function approximation'); legend('y = sin(x)', 'MLP approximation'); %% Generate the plot for the RNN figure plot(xextended(101:(length(xextended))),yextended(101:(length(yextended))),'Color',[1 0 0], 'LineWidth',2) hold on plot(xextended(101:(length(xextended))),rnnoutput, 'Color', [0 0 1], 'LineWidth',2) line([-2*pi -2*pi], [-2 2], 'Color', [0 0 0]); line([2*pi 2*pi], [-2 2], 'Color', [0 0 0]); xlabel('-4\pi < x < 4\pi'); % x-axis label ylabel('sine function approximation'); legend('y = sin(x)', 'RNN approximation');
