💥1 概述
应用背景：
通过分析序列进行合理预测，做到提前掌握未来的发展趋势，为业务决策提供依据，这也是决策科学化的前提。
时间序列分析：
时间序列就是按时间顺序排列的一组数据序列。
时间序列分析就是发现这组数据的变动规律并用于预测的统计技术。
一、时间序列分析简介
时间序列分析有三个基本特点：
- 假设事物发展趋势会延伸到未来
- 预测所依据的数据具有不规则性
- 不考虑事物发展之间的因果关系
📚2 运行结果
🎉3 参考文献
[1]杨学威.基于机器学习算法的股指期货价格预测模型研究[J].软件工程,2022,25(12):1-8.DOI:10.19644/j.cnki.issn2096-1472.2022.012.001.
👨💻4 Matlab代码
%% Machine Learning Online Class - Exercise 1: Linear Regression % Instructions % ------------ % % This file contains code that helps you get started on the % linear exercise. You will need to complete the following functions % in this exericse: % % warmUpExercise.m % plotData.m % gradientDescent.m % computeCost.m % gradientDescentMulti.m % computeCostMulti.m % featureNormalize.m % normalEqn.m % % For this exercise, you will not need to change any code in this file, % or any other files other than those mentioned above. % %
主函数部分代码：
%% Initialization clear ; close all; clc %% ======================= Part 2: Plotting ======================= fprintf('Plotting Data ...\n') data = load('P1.txt'); X = data(:, 1); y = data(:, 2); m = length(y); % number of training examples % Plot Data % Note: You have to complete the code in plotData.m plotData(X, y); fprintf('Program paused. Press enter to continue.\n'); pause; %% =================== Part 3: Cost and Gradient descent =================== X = [ones(m, 1), data(:,1)]; % Add a column of ones to x theta = zeros(2, 1); % initialize fitting parameters % Some gradient descent settings iterations = 1500; alpha = 0.01; fprintf('\nTesting the cost function ...\n') % compute and display initial cost J = computeCost(X, y, theta); fprintf('With theta = [0 ; 0]\nCost computed = %f\n', J); % further testing of the cost function J = computeCost(X, y, [-1 ; 2]); fprintf('\nWith theta = [-1 ; 2]\nCost computed = %f\n', J); fprintf('Program paused. Press enter to continue.\n'); pause; fprintf('\nRunning Gradient Descent ...\n') % run gradient descent theta = gradientDescent(X, y, theta, alpha, iterations); % print theta to screen fprintf('Theta found by gradient descent:\n'); fprintf('%f\n', theta); %fprintf('Expected theta values (approx)\n'); % Plot the linear fit hold on; % keep previous plot visible plot(X(:,2), X*theta, '-') legend('Training data', 'Linear regression') hold off % don't overlay any more plots on this figure fprintf('Program paused. Press enter to continue.\n'); pause; %% ============= Part 4: Visualizing J(theta_0, theta_1) ============= fprintf('Visualizing J(theta_0, theta_1) ...\n') % Grid over which we will calculate J theta0_vals = linspace(-10, 10, 100); theta1_vals = linspace(-1, 4, 100); % initialize J_vals to a matrix of 0's J_vals = zeros(length(theta0_vals), length(theta1_vals)); % Fill out J_vals for i = 1:length(theta0_vals) for j = 1:length(theta1_vals) t = [theta0_vals(i); theta1_vals(j)]; J_vals(i,j) = computeCost(X, y, t); end end
