In a tslm decomposition, the season variable is treaded as:

Question

BlackTom AI · Accepted Answer

The question asks about how the season component is treated in a tslm decomposition. 
Option 1: 'a reference variable.' In time series modeling, a reference variable would imply a fixed baseline or comparator, which is not how seasonal effects are typically encoded in tslm; seasons are usually captured as discrete, separate indicators rather than a single reference value. This makes this option unsuitable. 
Option 2: 'a time variable.' While time indices are involved in time series models, seasonality is not generally represented as just a continuous time variable; seasonal patterns recur at fixed intervals and are better captured with categorical indicators rather than a monotonic time trend. Therefore this option mischaracterizes the standard treatment of seasonality. 
Option 3: 'a dummy variable.' This is the conventional approach: seasonality is represented by a set of dummy (indicator) variables, each corresponding to a season or month, allowing the model to adjust the intercept (and possibly slope) for each seasonal period without imposing a linear time trend within seasons. This aligns with common tslm practice for capturing recurring seasonal effects. 
Option 4: 'a class variable.' The term 'class variable' is not typically used in this context within tslm decomposition; seasons are not treated as a categorical class in the sense of general class labels unrelated to time periods, and this wording does not reflect the standard encoding approach for seasonality. Hence this option is incorrect. 
Overall, the reasoning shows that the season component is modeled using dummy variables to capture distinct seasonal effects across periods, rather than as a reference, time, or class variable, which do not suitably encode periodic seasonal structure.

In a tslm decomposition, the season variable is treaded as:

查看解析

登录即可查看完整答案

类似问题

If you perform a classical additive decomposition, how are the components combined?

What is a limitation of the decomposition model?

When using classical decompose(), how is the trend-cycle component typically estimated?

You observe a time series plot where the seasonal variation seems to grow as the level of the series increases. Which decomposition and transformation combination is most appropriate to start with?

After decomposing a monthly time series of retail sales with tslm(), the seasonal component for March is 1.15 in a additive model. How is this value interpreted?

You have a ts object named my_ts with a frequency of 12. Which R code correctly performs an additive decomposition?

Recall the seasonal decomposition method in a time-series analysis we did in lecture. We computed the seasonal index for each quarter. This steps allows us to:

Put the following steps of classical decomposition in order: 1: Step 1 2: Step 2 3: Step 3 4: Step 4

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单