Summary Chapter 17 (SUT 2.3):Time Series Analysis and Forecasting
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Module
STK121 (STK121)
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University Of Pretoria (UP)
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Modern Business Statistics with Microsoft Excel
Chapter 17 (SUT 2.3): Time Series Analysis and Forecasting
Forecasting Methods
Time Series
Moving Averages
Trend projections
Time series decomposition
Tutorial Test 5
all excel treatments
Tabular and Geographical Displays of Data (STK110)
Chapter 18: Non parametric methods Study Unit 2.4
Statistics (STK) 110 CH 1- 5 Notes
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Chapter 17
Study Unit 2.3
TIME SERIES ANALYSIS AND FORECASTING
1
,Study Unit 2.3.1
Forecasting Methods: Qualitative
Forecasting methods can be classified as qualitative or quantitative.
Qualitative methods are generally involving the use of expert judgement to develop
forecasts.
Such methods are appropriate when historical data on the variable being forecast are either
not applicable or unavailable.
We will focus exclusively on quantitative forecasting methods in this chapter.
Forecasting methods: Quantitative
Quantitative forecasting methods can be used when:
o Past information about the variable being forecast is available.
o The information can be quantified and,
o Reasonable to assume the pattern of the past will continue.
In such cases, a forecast can be developed using a time series method or a casual method.
A time series is a set of observations measured at successive points in time or over
successive periods of time.
If the historical data used are restricted to past values of the series that we are trying to
forecast, the procedure is called Time Series Method.
If the historical data used involve other time series that are believed to be related to the
time series that we trying to forecast, the procedure is called a Causal Method.
→ Time Series Analysis
o Objective: is to discover a pattern in the historical data or time series and then
extrapolate the pattern into the future.
o The forecast is based solely on past values of the variable and/or past forecast
errors.
→ Causal Method
o Based on the assumption that the variable we are forecasting has a cause-effect
relationship with one or more other variables.
o Looking at regression analysis as a forecasting tool, we can view the time series
value that we want to forecast as a dependent variable.
o If we can identify a good set of related independent, or explanatory, variable we
may be able to develop an estimated regression equation for forecasting the time
series.
→ Regression Analysis
o By treating time as the independent variable and the time series as a dependent
variable, regression analysis can also be used as a time series method.
2
, o Time-series regression- refers to the use of regression analysis when the sole
independent variable is time.
o Cross-sectional regression refers to the use of regression analysis when the
independent variable(s) is (are) something other than time.
Time Series Pattern
A time series is a sequence of measurements taken every hour, day, week, month, quarter,
year or at any other regular time interval.
The pattern of data is an important factor in understanding how the time series has
behaved in the past.
If such behaviour can be expected to continue in the future, we can use it to guide us in
selecting an appropriate forecasting method.
Time series plot
o A useful first step in selecting a appropriate forecasting method is to construct a
time series plot.
o A time series plot is a graphical presentation of the relationship between time and
the time series variable.
o Time is on horizontal axis, and the time series values are shown on the vertical axis.
Example (time series plot)
Week 1 2 3 4 5 6 7 8 9 10 11 12
Sales 17 21 19 23 18 16 20 18 22 20 15 22
The number of gallons of gasoline sold by a gasoline distributor over a period of 12 weeks is
given in the table below. The distributor would like to identify the underlying pattern in the
data to guide it in selecting an appropriate forecasting method.
3
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