# Forecasting

Topics: Time series analysis, Forecasting, Time series Pages: 8 (1133 words) Published: September 13, 2013
Forecasting

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 Squared Error (MSE)
oMean Absolute Percentage Error (MAPE)

Forecast Control Measure
oTracking Signal

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...