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B. Random Fluctuation
The Objective of the smoothing method is to smooth out Random Fluctuation.
Each technique is referred to as a "smoothing approach" since its goal is to "smooth out" the time series' unpredictable fluctuations. These techniques are user-friendly and typically offer a high level of accuracy for short-range forecasts, such as a forecast for the upcoming time period. To eliminate random oscillations, smoothing techniques are used. All options are valid when choosing an alpha value for exponential smoothing. The equation yields a time series linear trend in thousands of dollars. While smoothing doesn't provide us with a model, it might be a helpful first step in characterizing different elements of the series. Data smoothing is used to account for the impacts of seasonality and ignore one-time outliers.
The surface's boundary, however, can have tangential noise that has not been smoothed. Smoothing with exponential growth and moving averages. A moving average smoothes a series by combining the monthly data points into longer time units, such as the average of several months' worth of data.
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