Forecasting analysis is used to predict future events. The first type of model is when past data (sales) are used to predict the future (demand). This is termed time series analysis, which includes the naive method, moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend, trend analysis. Such forecasting techniques are used widely in business applications and it is of importance to know which techniques to use depending on the data characteristics and application. Choosing the wrong techniques may results in inaccurate forecasts. ChiSquares offer myriad of forecasting techniques ranging from moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend , seasonal forecasting and trend analysis.
Demand(Y) = 10411.21 + 366.34 * Time(x) , MAD = 826.27 , MAPE = 0.07
Exponential Smoothing With Trend Analysis
Alpha = 0.05 , Beta = 0.05
Demand (y^) = 16415.54 , MAD = 1054.64 , MAPE = 0.08
1) Impact Of Poor Forecasting (1) – Impact Of Poor Forecasting
2) Impact Of Poor Forecasing (2) – Accountancy’s Age (KPMG’s Research)
3) Ten Worst Practices and Best Practices Of Demand Forecasting , Delphus Inc – Ten Worst (and some Best) Demand Forecasting Practices