基于机器学习算法与历史数据预测未来的站点关闭（Matlab代码实现）

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概述

????1 概述

????2 运行结果

????3 参考文献

????‍????4 Matlab代码

????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 = %fn', J);

% further testing of the cost function
J = computeCost(X, y, [-1 ; 2]);
fprintf('nWith theta = [-1 ; 2]nCost computed = %fn', 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('%fn', 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