临平区文化活动时序趋势数据

本数据根据临平区文化活动的历史趋势预测未来活动参与人数，帮助活动组织者规划活动规模和资源分配。有助于文化活动组织者根据节假日效应影响评估结果的结果，优先安排受欢迎的活动类型在节假日，以增加参与度和提高活动的影响力。有助于活动的市场营销团队和广告公司结合预测数据，制定或调整营销活动策略，提升营销效果。步骤1，数据收集和预处理：从公司文化保障卡服务系统中自动抽取临平区域的文化活动相关数据，包括活动ID、活动名称、活动类型、活动地点、活动时间、参与人数、天气情况、是否节假日。通过数据清洗去除无效或错误记录，确保数据质量。
步骤2，时间序列分析：使用ARIMA模型（一种时间序列预测方法），对参与人数进行ADF测试，判断时间序列的平稳性，通过ACF和PACF图确定ARIMA模型的参数并进行模型训练，基于模型进行未来趋势预测，输出预测日期的预测参与人数。
步骤3，节假日效应分析：采用逻辑回归模型，选择“是否节假日”数据作为特征，预测节假日对参与度的正向影响。
步骤4，天气影响分析：采用多元回归模型，将天气情况数据作为特征，使用多元线性回归模型训练数据，预测天气对参与度的影响，并根据天气情况对应的回归系数及其显著性进行分级判定，输出""正面""、“轻微正面”、“中性”、轻微负面”、“负面”五类天气影响评估结果。

This dataset is designed to predict future participant counts based on historical trends of cultural activities in Linping District, assisting event organizers in planning event scales and allocating resources. It helps cultural activity organizers prioritize scheduling popular event types during holidays based on the results of holiday effect impact assessments, thereby increasing participation and enhancing event influence. It also enables event marketing teams and advertising companies to formulate or adjust marketing strategies using the predictive data, improving marketing effectiveness.
Step 1, Data Collection and Preprocessing: Automatically extract cultural activity-related data for the Linping area from the company's Cultural Security Card service system, including activity ID, activity name, activity type, activity location, activity time, number of participants, weather conditions, and whether it is a holiday. Perform data cleaning to remove invalid or erroneous records and ensure data quality.
Step 2, Time Series Analysis: Use the ARIMA model (a time series prediction method) to conduct an Augmented Dickey-Fuller (ADF) test on the number of participants to determine the stationarity of the time series. Determine the parameters of the ARIMA model via ACF and PACF plots, train the model, conduct future trend prediction based on the trained model, and output the predicted number of participants for the target dates.
Step 3, Holiday Effect Analysis: Adopt a logistic regression model, taking the "whether it is a holiday" data as a feature, to predict the positive impact of holidays on participation.
Step 4, Weather Impact Analysis: Adopt a multiple regression model, taking weather condition data as features, train the model using a multiple linear regression framework to predict the impact of weather on participation. Conduct graded assessment based on the regression coefficients and their significance corresponding to different weather conditions, and output five types of weather impact evaluation results: "Positive", "Slightly Positive", "Neutral", "Slightly Negative", and "Negative".

搜集汇总

数据集介绍

特点

该数据集包含2138条临平区文化活动的时序数据，数据来源于自行产生，更新频次为按需更新。数据集通过ARIMA模型进行时间序列分析，预测未来活动参与人数，并结合节假日和天气影响评估，帮助活动组织者优化活动规划和营销策略。