This article provides readers and more especially business managers with an overview of moving average, exponential smoothing, trend analysis, and linear regression approaches to forecasting. The authors then provide specific examples for each approach and the Excel formulas necessary to develop effective forecasts. This is an important contribution to the literature because it demonstrates that businesses with limited resources can develop reliable and accurate forecasts in a timely and cost effective manner using readily available software.
Download Free PDF View PDF
Download Free PDF View PDF
Download Free PDF View PDF
In this article, we utilize the famous Exponential Smoothing Methods (ESM) family of Time Series (TS) forecast algorithms reviewed in Rahardja (2020), as an easy-and-quick way to forecast in Excel (version 2016 and above) software while taking into account any existence of level (intercept), trend (slope), and seasonality components into the model. We implement such ESM-family forecasting via the Microsoft Excel (2019) built-in function "FORECAST.ETS". Familiarity with the Box-Jenkins methods (1976) is not required to forecast via such Excel built-in function (version 2016 and above). For an illustration, we apply such Excel (2019) TS forecast function to a sales data-series example.
Download Free PDF View PDF
Download Free PDF View PDF
Journal of Statistical Software
Download Free PDF View PDF
