You have a historical dataset of monthly expenses and want to forecast next month's values. Which modeling approach is appropriate?

• A. Classification
• B. Regression
• C. Forecasting
• D. Clustering

Answer: (C)

Forecasting is about predicting future values in a time-ordered series. With a history of monthly expenses, you want to estimate the next month’s amount by learning patterns over time, such as trends and seasonality, and how observations relate to each other over the lagged months. That temporal structure is exactly what forecasting models are designed to handle, making them the best fit for this task.

Classification would assign labels to data, not a numeric amount to the next month. Regression can predict a numeric value, but it typically relies on static features rather than directly modeling the time-dependent patterns in a sequence. Clustering groups similar observations but does not produce a forecast for future values. For monthly data with a time component, forecasting methods like ARIMA, exponential smoothing, or Prophet are the standard choice.