Which of the following forecasting methods is most suitable for data with a linear trend but no seasonality?

Holt’s Linear Trend Method is an advanced form of exponential smoothing that captures both the level and the linear trend of time series data. It is ideal for datasets that exhibit a consistent upward or downward trend without any seasonal components. For instance, this method can effectively forecast monthly sales that steadily increase over time. It extends basic exponential smoothing by introducing a trend component, improving its forecasting accuracy. Why Other Options Are Wrong : A) ARIMA : ARIMA is versatile but requires data to be stationary and may not handle a simple linear trend as efficiently as Holt's method. B) Simple Moving Averages : This averages recent data points but does not explicitly account for trends, making it less suitable for linear trend forecasting. D) Seasonal Decomposition : This method is more relevant when seasonality is present, which is not the case here. E) Exponential Smoothing with Additive Seasonality : This method is specific to data with both trends and seasonal patterns, which are absent in this scenario.