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Explanation: ARIMA is a robust model suited for non-seasonal data forecasting, particularly when historical patterns like trends or moving averages are predictive of future values. Stock prices, while often influenced by external market conditions, exhibit patterns that can be effectively modeled using ARIMA after ensuring data stationarity. ARIMA leverages the autoregressive (AR) and moving average (MA) components to model trends and shocks in the data while integrating (I) differences to handle non-stationarity. Option A: Seasonal data is better handled by SARIMA, an extension of ARIMA. Option B: Temperature anomalies require specialized models for rare event detection, not ARIMA. Option D: Real-time data often involves streaming techniques beyond ARIMA’s scope. Option E: Periodic fluctuations fit SARIMA or exponential smoothing better than ARIMA.
The Marginal Standing Facility (MSF) rate is linked to which of the following rates?
_______ has got approval for the complete acquisition of Ohm Global Mobility Private (OHM) from OHM International Mobility for a nominal consideration...
Which organization is primarily responsible for ensuring financial inclusion in India?
Which financial instrument provides the right, but not the obligation, to buy or sell an asset at a specified price?
Which act governs the regulation and supervision of NonBanking Financial Companies (NBFCs) in India?
Under the Liberalised Remittance Scheme, all resident individuals, including minors, are allowed to freely remit up to USD ______ per financial year.
USSR was disintegrated in the period ______?
Which of the following banks continue to be identified by Reserve Bank of India as Domestic-Systemically important Banks?
The Rupee's _______ against a basket of currencies has increased, indicating that the Rupee has strengthened against major trading partners.
Which of the following instruments is considered an alternative to cash in the Indian payment system?