Introduction to Forecasting
Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are
supported by human judgment and intuition.
Introduction to Forecasting
Introduction to Forecasting
• Business forecasting generally attempts to predict future customer demand for a firm’s goods or services
• Macroeconomic forecasting attempts to predict future behavior of the economy and identify business cycle turning points.
Applications of forecasting
Operations management: forecast of product sales; demand for services
Marketing: forecast of sales response to advertisement procedures, new promotions etc.
Finance & Risk management: forecast returns from investments Economics: forecast of major economic variables, e.g. GDP,
population growth, unemployment rates, inflation; useful for monetary & fiscal policy; budgeting plans & decisions
Industrial Process Control: forecasts of the quality characteristics of a production process
Demography: forecast of population; of demographic events (deaths, births, migration); useful for policy planning
Forecast Methodstwo types of
forecasting methods: Qualitative methods are based on: -judgement -opinion -past experience -best guesses
Qualitative Techniques
• Delphi method (technological forecasting)
• Market research
• Panel of consensus (CEFC in Maine)
• Visionary forecasts
• Historical analogies
Useful for long forecast horizons and/or when the amount of historical data is limited.
Quantitative [Statistical] Techniques
Stochastic methods including:
summary statistics; moving averages; exponential smoothing; time series decomposition; regression models; trend projections; Box-Jenkins methodology.
Make use of historical data & of a forecasting model.
The three most widely used forecasting models -time series -Smoothing models - regression
Components of Demand Forecasting2 main factors help determine the type of forecasting method to be used: - Time Frame - Behavior & Possible Existence of Patterns
Short to Mid-Range forecasts : from daily to up to two years in length. commonly used to determine production and delivery schedules and to establish inventory levels. Long-Range forecasts : over two years into the future. usually used for strategic planning ; establish long-term goals, plan new products, enter new markets and develop new facilities & technology.
Definitions
Time Series A set of chronologically ordered points of
data.
In forecasting a time series it is generally assumed that factors which caused demand in the past will persist into the future.
Definitions
Decomposition Techniques Separating a time series into several
unobservable components, generally in an additive or multiplicative fashion.
Such components usually include a trend, seasonal, cycle, and residual or irregular.
Definitions
Seasonal Component Regularly occurring, systematic variation in
a time series according to the time of year.
Not found in annual data, or data of lower frequencies.
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1975 1980 1985 1990 1995 2000 2005
Residential Electricity Sales in Maine
(Millions of kWh)
January 1973 - May 2005
260
280
300
320
340
360
380
400
1995 1996 1997 1998 1999
Ice Storm in
January 1998
Residential Electricity Sales in Maine
1995 - 1999
Residential Electricity Demand in Maine Seasonal Means
220
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260
280
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360
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Definitions
Trend Component The tendency of a variable to grow over time,
either positively or negatively.
Residential Electricity Demand in Maine (millions of kWh)
150
200
250
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450
74 76 78 80 82 84 86 88 90 92 94 96 98
Definitions
Cycle Cyclical patterns in a time series which are
generally irregular in depth and duration.
Such cycles often correspond to periods of economic expansion or contraction.
Also know as the business cycle.
The Solar Cycle: 1749 - 1999 Sunspot activity peaks about every 11 years.
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60 80 00 20 40 60 80 00 20 40 60 80 00
8000
10000
12000
14000
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20000
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10 20 30 40 50 60 70 80 90 00
US Residents
Less Than 5 Years Old
The Business Cycle Real GDP: 1947 - 1999
(billions of 1992 dollars)
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2000
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8000
50 55 60 65 70 75 80 85 90 95
Nominal GDP: 1990 - 1997 There are seasonal variations in GDP.
5 0 0 0
6 0 0 0
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8 0 0 0
9 0 0 0
1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7
Definitions
Irregular Component The unexplained variation in a time series.
Residential Electricity Demand in Maine Irregular Component
-40
-20
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90 91 92 93 94 95 96 97 98 99
Examples of Time Series Behavior A trend is a gradual, long-term, upward or downward movement in demand. A current trend is the steady increase in sales of personal computers over the past few years. A cycle is an up-and-down movement in demand that repeats itself over a longer time span. Automotive sales often behave in a cyclical pattern. A seasonal pattern is a repetitive movement in demand that occurs periodically. Sales of winter sports equipment is seasonal by nature.
Problem Definition
• Expectations of customer
• Ask questions: - Desired form of forecast (e.g. monthly forecasts)
- Forecast horizon
- Forecast interval (how often to be revised)
Inputs to the Forecasting Process (data collection)
• Finding sources of data about the item to be forecast.
• Obtaining information about external conditions --- those factors in the environment that will influence a forecast.
• Determining the needs of the user of the forecast.
Inputs to the Forecasting Process (data collection)
• Putting together the human & financial resources required to produce a forecast.
• Listing the available alternatives for forecasting techniques.
Data Analysis
Identify the components of a time series.
• Trend: Does the series exhibit some slope when graphed?
• Seasonal: Does the series exhibit regular peaks and troughs during the year?
• Cycle: Are there identifiable cycles which last longer than 1 year?
Data Analysis
Identify the components of a time series.
• Irregular: Are there observations which cannot be associated with either the trend or seasonal components?
(The Ice Storm of 1998)
• Looking for irregularities is the primary focus of data analysis.
Data Quality
• Check for accuracy.
• Check for conformity: the data must adequately represent the phenomenon for which it is being used.
Macroeconomic indicators should reflect business cycles.
Data Quality
• Timeliness: When are the data available?
• Preliminary versus revised data?
• Are the data consistent across time?
BLS is experimenting with new measures of employment and the CPI that are not
completely consistent with historical data.
Exploratory Data Analysis
• Begin with a plot for your series and look for predominant features.
• Calculate summary statistics for the series.
• Fit a time trend to the series and look for outliers. Calculate (fitted - actual) values.
• Can you decompose the series?
Model Selection & Fitting
• Choose one or more forecasting models
• Fit the model to the data (estimate the unknown parameters, e.g. OLS)
• Evaluate the quality of the model fit
Model validation
• Examine fit to historical data
• Examine magnitude of forecast errors
• Evaluate the quality of the model fit
The forecasting process