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    Question

    Which time series application would most likely require

    ARIMA modeling for accurate forecasting?
    A Seasonal rainfall prediction. Correct Answer Incorrect Answer
    B Identifying temperature anomalies. Correct Answer Incorrect Answer
    C Stock price prediction with non-seasonal data Correct Answer Incorrect Answer
    D Monitoring real-time electricity usage patterns. Correct Answer Incorrect Answer
    E Detecting periodic fluctuations in retail sales. Correct Answer Incorrect Answer

    Solution

    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.

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