Oman College of Management
and Technology
Course 803303 – PIS
Chapter 3
Forecasting Techniques
CS/MIS Department
Definitions:
Forecasting: a
prediction of future
events used for planning purposes.
2
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.
3
Time Series Forecasts
4
Trend - long-term movement in data
Seasonality - short-term regular variations in data and
related to certain time periods
Irregular variations - caused by unusual circumstances
(pattern)
Random variations - caused by chance (Causality)
Forecast Variations
Irregular
variation
Trend
Cycles
90
89
88
Seasonal variations
5
Forecasting Techniques
Moving
average
Weighted
moving average
Exponential
Trend
and associative forecasting
Pyramid
6
smoothing
Forecasting
Simple Moving Average
Actual
47
MA5
45
43
41
39
37
MA3
35
1
2
3
4
5
6
7
8
9
n
MAn =
7
Ai
∑
i=1
n
10 11 12
Exponential Smoothing
Ft = Ft-1 + α(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.
8
Example of Exponential Smoothing
Period
1
2
3
4
5
6
7
8
9
10
11
12
9
Actual
42
40
43
40
41
39
46
44
45
38
40
Alpha = 0.1
Error
Alpha = 0.4
Error
42
41.8
41.92
41.73
41.66
41.39
41.85
42.07
42.36
41.92
41.73
-2.00
1.20
-1.92
-0.73
-2.66
4.61
2.15
2.93
-4.36
-1.92
42
41.2
41.92
41.15
41.09
40.25
42.55
43.13
43.88
41.53
40.92
-2
1.8
-1.92
-0.15
-2.09
5.75
1.45
1.87
-5.88
-1.53
Picking a Smoothing Constant
Actual
Dema
Demand
50
α = .1
45
40
α = .4
35
1
2
3
4
5
6
7
8
9 10 11 12
Period
10
MIS 381 - Topic # 3
Common Nonlinear Trends
Parabolic
Exponential
Growth
11
Associative Forecasting
12
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
Fitting the line
X
7
2
6
4
14
15
16
12
14
20
15
7
13
Y
15
10
13
15
25
27
24
20
27
44
34
17
Computed
relationship
50
40
30
20
10
0
0
5
10
15
20
25
Linear Trend Equation
Y
Yt = a + bt
0 1 2 3 4 5
14
t
a is the intercept & b is the slope.
Since it is calculated with the variability of the data
in mind, its formulation is not as straight-forward as
our usual notion of slope.
Calculating a and b
n ∑ (ty) - ∑ t ∑ y
b =
n ∑ t 2 - ( ∑ t) 2
∑ y - b∑ t
a =
n
15
Linear Trend Example
t
Week
1
2
3
4
5
2
t
1
4
9
16
25
2
Σ t = 15
Σ t = 55
2
(Σ t) = 225
16
y
Sales
150
157
162
166
177
ty
150
314
486
664
885
Σ y = 812 Σ ty = 2499
Linear Trend Calculation
b =
5 (2499) - 15(812)
5(55) - 225
=
12495-12180
275 -225
812 - 6.3(15)
a =
= 143.5
5
y = 143.5 + 6.3t
17
= 6.3
Forecast Accuracy
Error - difference between actual value and
predicted value
Mean Absolute Deviation (MAD)
Mean squared error (MSE)
a systematic error of forecasting
Tracking signal
18
Average of squared error
BIAS:
Average absolute error
Ratio of cumulative error and MAD
MAD & MSE
∑ Actual −forecast
MAD =
n
2
(Actual− forecast)
∑
MSE =
n -1
19
Tracking Signal
(Actual-forecast)
∑
Tracking signal =
MAD
(Actual-forecast)
∑
Tracking signal =
∑ Actual-forecast
n
20
Tracking signal example
Period
Forecast
Actual
1
2
3
4
100
100
100
100
MAD =
96
98
104
110
2
21
Cumulative Tracking
Deviation Deviation
signal
5
2.5
-4
1
0.5
-2
-1
-0.5
4
3
1.5
10
13
6.5
Track limit=
±4
Accuracy????
Product B
Product A
30
30
25
25
20
20
15
15
10
10
5
5
Forecast
Actual
Forecast
Actual
• Which is more accurate?
• Which can be fixed more easily?
• Which is more “dangerous?”
22
“Radical” Thoughts on Forecasting:
Forecasting
is a Process
Don’t
Focus on Forecast Accuracy
Focus
on Bias – Stamp It Out
23
The Forecasting Process
Outputs
Inputs
Current Customers
New Customers
Competition
Economic Outlook
New Products
Pricing Strategy
Promotions
Bid Activity
Management
Directives
Intra-Company Demand
History (Data)
Other
Forecasts that are:
Process
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1. Reasoned
2. Reasonable
3. Reviewed
Frequently
4. Represent the
Total Demand
Forecasts for Strategic Business
Planning
25
Are used for long term broad based forecasts –
capital expansion, new product line, merger or
acquisition decisions.
Usually use causal models and regression analysis
Concluding Principles
Forecasting models should not be more
complicated than necessary. Simple models work
just as good.
Input data and output forecasts should be routinely
monitored for quality and appropriateness.
Information on sources of variation should be
incorporated into the forecasting system.
Forecast from different sources must be reconciled
and made consistent with firm plans and
constraints.
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