Start learning 50% faster. Sign in now
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.
If the supply of sugar increases in a market in equilibrium, the equilibrium price will _______ and the equilibrium quantity will _______.
What types of income are included in the current account of the Balance of Payments (BoP)?
When to accomplish a particular necessity, the Demand of various goods is increased automatically into the market , it is known as ________________ .
National Income is the
The Inflation caused by an increase in prices of inputs like labour, raw material, etc. is known as:
Which Five Year Plan in India has the tagline – Faster Sustainable and more inclusive growth?
Pradhan Mantri Ujjwala Yojana is related with
The Stand-Up India Scheme facilitates bank loans between what amounts for setting up a greenfield enterprise by at least one SC/ST and one woman borrowe...
A minimum wage is defined as:
Which feature is not typically associated with the manufacturing sector in India?