# Forecasting

**Topics:**Time series analysis, Forecasting, Time series

**Pages:**8 (1133 words)

**Published:**September 13, 2013

Why forecast?

Features Common to all Forecasts

•Conditions in the past will continue in the future

•Rarely perfect

•Forecasts for groups tend to be more accurate than forecasts for individuals •Forecast accuracy declines as time horizon increases

Elements of a Good Forecast

•Timely

•Accurate

•Reliable (should work consistently)

•Forecast expressed in meaningful units

•Communicated in writing

•Simple to understand and use

Steps in Forecasting Process

•Determine purpose of the forecast

•Establish a time horizon

•Select forecasting technique

•Gather and analyze the appropriate data

•Prepare the forecast

•Monitor the forecast

Types of Forecasts

•Qualitative

oJudgment and opinion

oSales force

oConsumer surveys

oDelphi technique

•Quantitative

oRegression and Correlation (associative)

oTime series

Forecasts Based on Time Series Data

•What is Time Series?

•Components (behavior) of Time Series data

oTrend

oCycle

oSeasonal

oIrregular

oRandom variations

Naïve Methods

Naïve Forecast – uses a single previous value of a time series as the basis of a forecast.

Techniques for Averaging

•What is the purpose of averaging?

•Common Averaging Techniques

oMoving Averages

oExponential smoothing

Moving Average

Exponential Smoothing

Techniques for Trend

Linear Trend Equation

Curvilinear Trend Equation

Techniques for Seasonality

•What is seasonality?

•What are seasonal relatives or indexes?

•How seasonal indexes are used:

oDeseasonalizing data

oSeasonalizing data

•How indexes are computed (see Example 7 on page 109)

Accuracy and Control of Forecasts

Measures of Accuracy

oMean Absolute Deviation (MAD)

oMean Squared Error (MSE)

oMean Absolute Percentage Error (MAPE)

Forecast Control Measure

oTracking Signal

Mean Absolute Deviation (MAD)

Mean Squared Error (or Deviation) (MSE)

Mean Square Percentage Error (MAPE)

Tracking Signal

Problems:

2 – Plot, Linear, MA, exponential Smoothing

5 – Applying a linear trend to forecast

15 – Computing seasonal relatives

17 – Using indexes to deseasonalize values

26 – Using MAD, MSE to measure forecast accuracy

Problem 2 (110)

National Mixer Inc., sells can openers. Monthly sales for a seven-month period were as follows:

MonthSales

(000 units)

Feb19

March18

April15

May20

June18

July22

August20

(a)Plot the monthly data on a sheet of graph paper.

(b)Forecast September sales volume using each of the following: (1)A linear trend equation

(2)A five-month moving average

(3)Exponential smoothing with a smoothing constant equal to 0.20, assuming March forecast of 19(000) (4)The Naïve Approach

(5)A weighted average using 0.60 for August, 0.30 for July, and 0.10 for June

(c)Which method seems least appropriate? Why?

(d)What does use of the term sales rather than demand presume?

EXCEL SOLUTION

(a) Plot of the monthly data

How to superimpose a trend line on the graph

•Click on the graph created above (note that when you do this an item called CHART will appear on the Excel menu bar) •Click on Chart > Add Trend Line

•Click on the most appropriate Trend Regression Type

•Click OK

(b) Forecast September sales volume using:

(1)Linear Trend Equation

•Create a column for time period (t) codes (see column B) •Click Tools > Data Analysis > Regression

•Fill in the appropriate information in the boxes in the Regression box that appears

(2)Five-month moving average

(3)Exponential Smoothing with a smoothing constant of 0.20, assuming March forecast of 19(000)

•Enter the smoothing factor in D1

•Enter “19” in D5 as forecast for March

•Create the exponential smoothing formula in D6, then copy it onto D7 to...

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