Final answer:
To forecast COVID mortality, which likely exhibits trends and seasonality, Holt-Winters might be the least biased method as it accounts for these aspects. Other methods like Moving Average, Exponential Smoothing, and ARIMA each have their own advantages, but Holt-Winters is specifically designed to handle data with trends and seasonal patterns.
Step-by-step explanation:
The question asks which time-series forecasting smoothing method is selected as least biased in tracking the recent movement of data to forecast COVID mortality. The four options given are: Moving Average, Exponential Smoothing, Holt-Winters, and ARIMA. Each of these methods has different characteristics and suitability based on the nature of the data and the goal of the analysis.
Moving Average is a simple technique that averages a set number of past data points to smooth out short-term fluctuations and highlight longer-term trends or cycles. It is good for a quick and rough approximation but may not be the best for data with trends or seasonal patterns.
Exponential Smoothing gives more weight to recent data and less weight to old data, allowing for a more responsive model to recent changes. It's simple to apply and can adapt to data with a level trend.
Holt-Winters extends exponential smoothing by accounting for both trend and seasonality in the data, making it more suitable for data with trends and seasonal patterns.
ARIMA (Autoregressive Integrated Moving Average) is a sophisticated method that uses past data to make predictions. It is very flexible and can model a wide variety of time series data but can be more complex to implement.
In the context of forecasting COVID mortality, which is likely to exhibit both trends and seasonality, Holt-Winters could be considered the least biased method as it accounts for both seasonal variations and trends. Nonetheless, the best method could vary depending on the specific characteristics of the data set and the forecasting objectives.
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