The document provides an overview of forecasting techniques. It defines a forecast as a statement about the future value of a variable of interest. Accurate forecasts are important for accounting, finance, human resources, marketing, operations and other business functions. The key types of forecasts discussed are judgmental forecasts, time series forecasts, and associative models. Time series techniques include naive methods, moving averages, weighted moving averages, and exponential smoothing. Accuracy is measured using metrics like mean absolute deviation, mean squared error and mean absolute percentage error. Choosing the right technique depends on factors like cost, required accuracy, available data and time horizon.
A method that uses measurable, historical data observations, to make forecasts by calculating the weighted average of the current period’s actual value and forecast, with a trend adjustment added in
Introduction - Objectives Of Studying Time Series Analysis - Variations In Time Series
- Methods Of Estimating Trend: Freehand Method - Moving Average Method - Semi-Average Method - Least
Square Method
A method that uses measurable, historical data observations, to make forecasts by calculating the weighted average of the current period’s actual value and forecast, with a trend adjustment added in
Introduction - Objectives Of Studying Time Series Analysis - Variations In Time Series
- Methods Of Estimating Trend: Freehand Method - Moving Average Method - Semi-Average Method - Least
Square Method
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2. 3-2 Forecasting
FORECAST:
A statement about the future value of a variable of
interest such as demand.
Forecasts affect decisions and activities throughout
an organization
Accounting, finance
Human resources
Marketing
MIS
Operations
Product / service design
3. 3-3 Forecasting
Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services
4. 3-4 Forecasting
Assumes causal system
past ==> future
Forecasts rarely perfect because of
randomness
Forecasts more accurate for
groups vs. individuals I see that you will
get an A this semester.
Forecast accuracy decreases
as time horizon increases
5. 3-5 Forecasting
Elements of a Good Forecast
Timely
Reliable Accurate
Written
6. 3-6 Forecasting
Steps in the Forecasting Process
“The forecast”
Step 6 Monitor the forecast
Step 5 Prepare the forecast
Step 4 Gather and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast
7. 3-7 Forecasting
Types of Forecasts
Judgmental - uses subjective inputs
Time series - uses historical data
assuming the future will be like the past
Associative models - uses explanatory
variables to predict the future
8. 3-8 Forecasting
Judgmental Forecasts
Executive opinions
Sales force opinions
Consumer surveys
Outside opinion
Delphi method
Opinions of managers and staff
Achieves a consensus forecast
9. 3-9 Forecasting
Time Series Forecasts
Trend - long-term movement in data
Seasonality - short-term regular variations in
data
Cycle – wavelike variations of more than one
year’s duration
Irregular variations - caused by unusual
circumstances
Random variations - caused by chance
11. 3-11 Forecasting
Naive Forecasts
Uh, give me a minute....
We sold 250 wheels last
week.... Now, next week
we should sell....
The forecast for any period equals
the previous period’s actual value.
12. 3-12 Forecasting
Naïve Forecasts
Simple to use
Virtually no cost
Quick and easy to prepare
Data analysis is nonexistent
Easily understandable
Cannot provide high accuracy
Can be a standard for accuracy
13. 3-13 Forecasting
Uses for Naïve Forecasts
Stable time series data
F(t) = A(t-1)
Seasonal variations
F(t) = A(t-n)
Data with trends
F(t) = A(t-1) + (A(t-1) – A(t-2))
14. 3-14 Forecasting
Techniques for Averaging
Moving average
Weighted moving average
Exponential smoothing
15. 3-15 Forecasting
Moving Averages
Moving average – A technique that averages a
number of recent actual values, updated as new
values become available.
n
1 Ai
i=
MAn =
n
Weighted moving average – More recent values in a
series are given more weight in computing the
forecast.
16. 3-16 Forecasting
Simple Moving Average
Actual
MA5
47
45
43
41
39
37 MA3
35
1 2 3 4 5 6 7 8 9 10 11 12
n
1 Ai
i=
MAn =
n
17. 3-17 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
• Premise--The most recent observations might
have the highest predictive value.
Therefore, we should give more weight to the
more recent time periods when forecasting.
18. 3-18 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
Weighted averaging method based on previous
forecast plus a percentage of the forecast error
A-F is the error term, is the % feedback
22. 3-22 Forecasting
Linear Trend Equation
Ft
Ft = a + bt
Ft = Forecast for period t 0 1 2 3 4 5 t
t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line
23. 3-23 Forecasting
Calculating a and b
n (ty) - t y
b =
n t 2 - ( t) 2
y - b t
a =
n
24. 3-24 Forecasting
Linear Trend Equation Example
t y
2
Week t Sales ty
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885
t = 15 t = 55 y = 812 ty = 2499
2
2
( t) = 225
25. 3-25 Forecasting
Linear Trend Calculation
5 (2499) - 15(812) 12495 -12180
b = = = 6.3
5(55) - 225 275 -225
812 - 6.3(15)
a = = 143.5
5
y = 143.5 + 6.3t
26. 3-26 Forecasting
Associative Forecasting
Predictor variables - used to predict values of
variable interest
Regression - technique for fitting a line to a set
of points
Least squares line - minimizes sum of squared
deviations around the line
27. 3-27 Forecasting
Linear Model Seems Reasonable
X Y Computed
7 15
relationship
2 10
6 13 50
4 15 40
14 25 30
15 27 20
10
16 24
0
12 20 0 5 10 15 20 25
14 27
20 44
15 34
7 17
A straight line is fitted to a set of sample points.
28. 3-28 Forecasting
Forecast Accuracy
Error - difference between actual value and predicted
value
Mean Absolute Deviation (MAD)
Average absolute error
Mean Squared Error (MSE)
Average of squared error
Mean Absolute Percent Error (MAPE)
Average absolute percent error
29. 3-29 Forecasting
MAD, MSE, and MAPE
Actual forecast
MAD =
n
2
( Actual forecast)
MSE =
n -1
( Actual forecas / Actual*100)
MAPE =
t
n
31. 3-31 Forecasting
Controlling the Forecast
Control chart
A visual tool for monitoring forecast errors
Used to detect non-randomness in errors
Forecasting errors are in control if
All errors are within the control limits
No patterns, such as trends or cycles, are
present
32. 3-32 Forecasting
Sources of Forecast errors
Model may be inadequate
Irregular variations
Incorrect use of forecasting technique
33. 3-33 Forecasting
Tracking Signal
•Tracking signal
–Ratio of cumulative error to MAD
Tracking signal =
(Actual-forecast)
MAD
Bias – Persistent tendency for forecasts to be
Greater or less than actual values.
34. 3-34 Forecasting
Choosing a Forecasting Technique
No single technique works in every situation
Two most important factors
Cost
Accuracy
Other factors include the availability of:
Historical data
Computers
Time needed to gather and analyze the data
Forecast horizon