Which time series application would most likely require ARIMA modeling for accurate forecasting?

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.