题目
题目

STAT_V 443 202 2024W2 Activity 5: Autoregressive Processes

单项选择题

(On Problem 6 of the worksheet) Is  { 𝑋 𝑡 } stationary?

选项
A.The process is stationary if  𝛼 < 1 .
B.The process is stationary provided 𝛼 > 0 .
C.The process is stationary.
D.The process is stationary provided  | 𝛼 | < 1 .
E.The process is non-stationary.
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标准答案
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思路分析
We start by recalling the typical condition for stationarity in a simple AR(1) process: X_t = α X_{t-1} + ε_t, where ε_t is a white noise error term. The process is weakly stationary if and only if the absolute value of the autoregressive coefficient is strictly less than 1, i.e., |α| < 1. With that in mind, let’s evaluate each option one by one. Option 1: 'The process is stationary if α < 1.' This statement misses the important absolute value ......Login to view full explanation

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Would it be convenient to use a machine learning model directly on the time series below?   I No, because the level of the time series is too high, going beyond 4000. II No, because the time window of the time series is too wide. III No, it would be better to test for the non-stationarity of the time series and transform it before applying any model. IV Yes, machine learning can deal with any type of time series without problems.  

What is the primary characteristic of a stationary time series?

How would you characterize such a time series :

A random walk process (no drift!) has mean zero, that's why it's weakly stationary but not strong (strict) stationary

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