atp1d

Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Airline Ticket Price dataset concerns the prediction of airline ticket prices. The rows are a sequence of time-ordered observations over several days. Each sample in this dataset represents a set of observations from a specific observation date and departure date pair. The input variables for each sample are values that may be useful for prediction of the airline ticket prices for a specific departure date. The target variables in these datasets are the next day (ATP1D) price or minimum price observed over the next 7 days (ATP7D) for 6 target flight preferences: (1) any airline with any number of stops, (2) any airline non-stop only, (3) Delta Airlines, (4) Continental Airlines, (5) Airtrain Airlines, and (6) United Airlines. The input variables include the following types: the number of days between the observation date and the departure date (1 feature), the boolean variables for day-of-the-week of the observation date (7 features), the complete enumeration of the following 4 values: (1) the minimum price, mean price, and number of quotes from (2) all airlines and from each airline quoting more than 50 % of the observation days (3) for non-stop, one-stop, and two-stop flights, (4) for the current day, previous day, and two days previous. The result is a feature set of 411 variables. For specific details on how these datasets are constructed please consult Groves and Gini (2015). The nature of these datasets is heterogeneous with a mixture of several types of variables including boolean variables, prices, and counts.

本多元回归数据集源自：https://link.springer.com/article/10.1007%2Fs10994-016-5546-z ：机票价格（Airline Ticket Price）数据集旨在预测航空机票价格。数据集的行按时间顺序排列，对应多日的观测序列。每个样本代表一组针对特定观测日期与出发日期组合的观测数据。每个样本的输入变量均为可用于预测特定出发日期机票价格的有效特征。
本数据集的目标变量为次日价格（ATP1D），或是未来7日内的最低价格（ATP7D），涵盖6类目标航班偏好场景：(1) 任意航司、任意经停次数；(2) 任意航司直飞航班；(3) 达美航空（Delta Airlines）；(4) 大陆航空（Continental Airlines）；(5) 机场快线航空（Airtrain Airlines）；(6) 联合航空（United Airlines）。
输入变量包含以下几类：观测日期与出发日期之间的天数（1个特征）；观测日期所属星期的布尔型变量（共7个特征）；对以下四类维度进行完整枚举的特征：(1) 所有航司，以及观测日报价量占比超过50%的各单航司的最低票价、平均票价与报价量；(2) 直飞、单次经停、两次经停的航班类别；(3) 观测当日、前一日、两日之前的时间窗口。最终该数据集共包含411个特征变量。
如需了解该数据集构建的具体细节，请参考Groves与Gini（2015）的研究。本数据集具有异质性特征，混合包含布尔型变量、票价数值与计数型变量等多种数据类型。