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Explanation: Stationarity in time series data is a critical assumption for applying ARIMA models. ARIMA (AutoRegressive Integrated Moving Average) is designed to work with data that has constant mean, variance, and autocovariance over time. Stationary data ensures the model's stability, enabling accurate predictions and parameter estimation. If the data is not stationary, the ARIMA model's results may be unreliable. Non-stationary data can lead to misleading forecasts, as the underlying patterns are not stable. Techniques like differencing, logarithmic transformations, or the Dickey-Fuller test are employed to achieve stationarity. Option A: While ARIMA addresses autocorrelation, stationarity is needed for foundational assumptions, not just for residual issues. Option B: Stationarity helps improve model accuracy but is not the primary reason for its necessity. Option D: Decomposition is a separate analytical step and not a requirement for ARIMA. Option E: Seasonal components are addressed by SARIMA models, not basic ARIMA.
The major maize producing districts in Rajasthan are -
Which earthquake measuring scale is based on Earth's rigidity and the amount and area of slip on the fault?
Which of the following is not correctly matched?
Norman Borlaug was given Nobel Prize in which field?
Which of the following pairs of State and formation year is/are correct?
I. Nagaland - 1972
II. Uttarakhand - 2000
III. Arunachal Pradesh - 1987
A. his subjects wisely
B. was a very kind and generous
C. king who looked after
D. everyone said that he
Where has the Constitution Park been inaugurated in Rajasthan by President Draupadi Murmu?
Which blood cells are called 'Soldiers' of the body?
What is the range of the intensity scale used in measuring earthquakes?