Forecast-Simple forecasting methods

PROBLEM DEFINITION

Primary objective of the analysis is to find the future sales of a product, so accordingly the inventory of product is managed according to future demand.

Data: For confidentiality purposes the name of the product is not revealed.

Sales Data set provider : Agri Village Products(M) SDN BHD

Software’s used for analysis: R, Excel

FORECAST(ETS)

Calculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline.

NAIVE FORECASTING

Estimating technique in which the last period's actuals are used as this period's forecast, without adjusting them or attempting to establish causal factors. It is used only for comparison with the forecasts generated by the better (sophisticated) techniques.

FORECAST FROM CUBIC SMOOTHING SPLINES

Cubic smoothing splines fitted to univariate time series data can be used to obtain local linear forecasts.

The advantage of the spline model over the full ARIMA model is that it provides a smooth historical trend as well as a linear forecast function.

FORECAST FROM RANDOM WALKS

One of the simplest and yet most important models in time series forecasting is therandom walk model. This model assumes that in each period the variable takes arandom step away from its previous value, and the steps are independently and identically distributed in size