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MATLAB基于云模型时间序列预测

news 2025/10/26 7:26:16

在这里插入图片描述
云模型基本理论
三个数字特征
期望(Ex)：概念的中心值

熵(En)：概念的不确定性度量

超熵(He)：熵的不确定性

云发生器
正向云发生器：从定性到定量的转换

逆向云发生器：从定量到定性的转换

在商品月销售量预测中的应用

数据预处理与云化处理
matlab

% 历史销售量数据云化function[Ex, En, He]=sales_cloud_model(historical_sales)% 计算三个数字特征 Ex =mean(historical_sales); En =sqrt(pi/2)*mean(abs(historical_sales - Ex)); He =sqrt(abs(var(historical_sales)- En^2));end

销售量状态划分
状态等级定义
matlab

sales_levels ={‘很低’,‘较低’,‘正常’,‘较高’,‘很高’};% 各状态对应的云模型参数level_params =[0.1,0.05,0.02;% 很低0.3,0.08,0.03;% 较低 0.5,0.10,0.04;% 正常0.7,0.08,0.03;% 较高0.9,0.05,0.02% 很高];

基于云推理的预测模型
云推理预测步骤
matlab

function predicted_sales =cloud_based_prediction(current_features, historical_data)% 步骤1: 特征云化 feature_clouds =extract_feature_clouds(current_features);% 步骤2: 相似度计算 similarity_scores =calculate_cloud_similarity(feature_clouds, historical_data);% 步骤3: 权重分配 weights =normalize_similarity(similarity_scores);% 步骤4: 云合成预测 predicted_cloud =cloud_synthesis(historical_data.sales_clouds, weights);% 步骤5: 云滴生成 predicted_sales =forward_cloud_generator(predicted_cloud,1000);end

季节性云模型
matlab

% 月度季节性云模型function monthly_clouds =build_seasonal_clouds(monthly_sales) monthly_clouds =cell(12,1);fori=1:12 month_data =monthly_sales(month(monthly_sales.dates)==i,:); monthly_clouds{i}=sales_cloud_model(month_data.sales);endend

混合云预测框架
完整预测流程
matlab

classdef SalesCloudPredictor properties historical_clouds seasonal_clouds trend_cloud external_factors end methods function obj =SalesCloudPredictor(historical_data)% 初始化各组件云模型 obj.historical_clouds =build_historical_clouds(historical_data); obj.seasonal_clouds =build_seasonal_clouds(historical_data); obj.trend_cloud =extract_trend_cloud(historical_data);endfunction prediction =predict(obj, current_month, features)% 多源云模型融合预测 base_cloud = obj.seasonal_clouds{current_month}; trend_effect =adjust_by_trend(base_cloud, obj.trend_cloud); external_effect =apply_external_factors(trend_effect, features);% 云合成最终预测 final_cloud =cloud_fusion({base_cloud, trend_effect, external_effect}); prediction =cloud_to_point_estimate(final_cloud);endendend

不确定性度量与置信区间
matlab

function[point_estimate, confidence_interval]=cloud_prediction_interval(cloud_params, alpha)% 基于云模型生成预测区间 Ex =cloud_params(1); En =cloud_params(2); He =cloud_params(3);% 计算置信边界 lower_bound = Ex -norminv(1-alpha/2)*sqrt(En^2+ He^2); upper_bound = Ex +norminv(1-alpha/2)*sqrt(En^2+ He^2); point_estimate = Ex; confidence_interval =[lower_bound, upper_bound];end

实际应用案例
零售商品月销量预测
matlab

% 实际应用示例functionmain_cloud_prediction()% 加载历史销售数据 sales_data =readtable(‘monthly_sales_data.csv’);% 构建云预测器 predictor =SalesCloudPredictor(sales_data);% 未来月份预测 future_months =6; predictions =zeros(future_months,3);% [预测值, 下限, 上限]fori=1:future_months target_month =mod(month(now)+i-1,12)+1; current_features =extract_current_features();[point_est, interval]= predictor.predict(target_month, current_features);predictions(i,:)=[point_est, interval];end% 可视化结果plot_cloud_predictions(sales_data, predictions);end

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