Stationarity in time series and why ARIMA needs it
Source: interviewintermediate
WHAT IT TESTS: whether you know stationarity means stable statistical properties over time. OUTLINE: constant mean/variance/autocovariance; ARIMA's coefficients assume them; test with the ADF test and ACF plots; achieve it via differencing or log transforms.
WHAT IT TESTS: whether you understand stationarity as statistical stability over time and can both detect and fix non-stationarity. ANSWER OUTLINE: a stationary series has constant mean, constant variance, and an autocovariance that depends only on lag not on time; ARIMA's fitted coefficients assume this stability, so trends or changing variance break forecasts.
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- #time-series
- #arima
- #stationarity
- #forecasting
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