Forecasting System

This system utilizes historical  data to determine the best algorithm and parameter combination to predict future values of time-series data.

ALGORITHMS UTILIZED 

Moving Average

• The simplest approach of all
• Assumes the data has no trend component
• Calculates the predicted value by averaging the n most recent data points
• Each historical value carries the same weight

Exponential Smoothing

• Assumes the data has no trend component
• Diminishes the weight of older data based on the value of a parameter  alpha (0 ≤ alpha ≤ 1)
• As alpha increases, it makes the predicted value more sensitive to each new data point
• Stable models use a lower value of alpha in order to make the prediction less responsive to changes

Exponential Smoothing with a Linear Trend

• Similar to exponential smoothing except it assumes that the data does contain a linearly-based trend component
• Uses alpha in the same way as exponential smoothing
• Also uses a beta value (0 ≤ beta ≤ 1) to adjust the sensitivity of the linear trend’s slope to new data

Least Squares

• Assumes that the model is based solely on a linear trend
• It minimizes the total of squared vertical differences between the data points and the regression line

Winters Method

• A derivation of exponential smoothing with a linear trend, except it uses a seasonality factor to track any cyclical trends
• Requires two complete cycles of historical data
• It incorporates the same alpha and beta values in the exponential smoothing with linear trend model
• Also uses a gamma value (0 ≤ gamma  ≤ 1) to adjust the sensitivity to cyclical trends
• A lower value of gamma will dampen the cyclical portion of the demand function

REFERENCES 

1. Sipper, Daniel & Bulfin, Jr., Robert L., Production Planning, Control, and Integration (New York, New York: McGraw-Hill, 1997)  
2. Lewis, William E., Software Testing and Continuous Quality Improvement (New York, New York: CRC Press LLC, 2000)  
3. Jacobs, F. Robert & Chase, Richard B., Operations and Supply Management: The Core (New York, New York: McGraw-Hill, 2008)  
4. Hopp, Wallace J. & Spearman, Mark L., Factory Physics (New York, New York: McGraw-Hill, 2001)  
5. Sipper, Daniel & Bulfin, Jr., Robert L., Production Planning, Control, and Integration (New York, New York: McGraw-Hill, 1997)