PROMO简单的因果效应在时间序列上的分析(PROMO Simple causal effects in time series).pdf

PROMO:简单的因果效应在时间序列上的分析 (PROMO: Simple causal effects in time series) 数据介绍： The PROMO dataset proposes the task to identify which promotions affect sales. Artificial data about 1000 promotion variables and 100 product sales is provided. The goal is to predict a 1000x100 boolean influence matrix, indicating for each (i,j) element whether the ith promotion has a causal influence of the sales of the jth product. Data is provided as time series, with a daily value for each variable for three years (i.e., 1095 days). Each of the 100 products has a defined seasonal baseline, repeating over the years. The seasonal effect can vary from almost inexistent to major. On top of this baseline are promotions. Each product is influenced by between 1 and 50 promotions out of the 1000 promotions available. Promotions usually increase the sales with respect to the baseline, but can occasionally reduce them (e.g., when a similar competing product is promoted, that promotion might have a negative effect on the sales of the current product). On top of that are daily variations. E 关键词： 时间序列,结构方程,模型,人工数据,因果效应, time series,structural equation,models,artificial data,causal effects, 数据格式： TEXT 数据详细介绍： PROMO: Simple causal effects in time series This dataset is proposed in the context of the Causality Workbench. Please also check out its page on the repository. Summary The PROMO dataset proposes the task to identify which promotions affect sales. Artificial data about 1000 promotion variables and 100 product sales is provided. The goal is to predict a 1000x100 boolean influence matrix, indicating for each (i,j ) element whether the ith promotion has a causal influence of the sales of the j th product. Data is provided as time series, with a daily value for each variable for three years (i.e., 1095 days). Each of the 100 products has a defined seasonal baseline, repeating over the years. The seasonal effect can vary fro