What is a limitation of the decomposition model?

Question

BlackTom AI · Accepted Answer

Consider the prompt: what is a limitation of the decomposition model? 
Option 1: 'It cannot be used if time series is stationary.' In fact, decomposition methods can be applied to stationary series as long as there is a systematic seasonal and trend structure; stationarity in itself does not preclude decomposition, so this statement is inaccurate. 
Option 2: 'It requires the seasonal component to be constant over time.' This is a key limitation often cited for classical additive (or multiplicative) decomposition: many forms assume that the seasonal pattern repeats identically over time, meaning the seasonal effect does not change shape or magnitude across periods. When seasonality evolves, the standard decomposition can misrepresent the data and reduce accuracy. 
Option 3: 'It is only applicable to quarterly data.' Decomposition is not restricted to quarterly data; it can be used with monthly, quarterly, weekly, or other seasonal frequencies, depending on the data and model; restricting it to quarterly data is incorrect. 
Option 4: 'It cannot be used if the trend is negative.' Negative trends do not inherently prevent decomposition; the method can accommodate decreasing trends, so this claim is false. 
Thus, the statement in Option 2 correctly identifies a common limitation: the assumption of a time-invariant seasonal component, which may not hold in evolving seasonal patterns.

What is a limitation of the decomposition model?

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?

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

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

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