Which forecasting method is most appropriate for time series data with a consistent trend but no seasonality?

Holt’s Linear Trend Model is designed specifically for time series data that exhibit a consistent trend without seasonal variations. This method extends simple exponential smoothing by adding a trend component, allowing it to capture upward or downward patterns over time effectively. It uses two smoothing equations: one for the level and another for the trend, making it highly suited for data where trend persists but no seasonal pattern exists. This targeted approach enables accurate forecasting of data that steadily increases or decreases, such as population growth or steady economic indicators. The other options are incorrect because: • Option 1 (Simple Moving Average) smooths data but does not account for trends. • Option 3 (Holt-Winters Seasonal Model) includes seasonal adjustment, unnecessary for non-seasonal data. • Option 4 (ARIMA) models trends and seasonality but can be overly complex for simple trends. • Option 5 (STL) is primarily for decomposition, best suited for data with both trend and seasonality.