Forecasting and Trend Analysis Paper presented at The 2014 Training Workshop of Committee of Directors of Academic Planning of Nigerian Universities By Samson Akinyosoye October 2014
Download ReportTranscript Forecasting and Trend Analysis Paper presented at The 2014 Training Workshop of Committee of Directors of Academic Planning of Nigerian Universities By Samson Akinyosoye October 2014
Slide 1
Forecasting
and Trend
Analysis
Paper presented at The 2014 Training Workshop of Committee of
Directors of Academic Planning of Nigerian Universities
By
Samson Akinyosoye
October 2014
Slide 2
Learning Objectives
This lecture will have the following broad objectives:
1.
2.
3.
4.
To understand the role of data in planning
To highlight the role of data in forecasting
To identify different data presentation for forecasting
To discuss simple forecasting and trend analysis
methods
5. To introduce participants to simple tools for forecasting
and trend analysis
Slide 3
Opening Vignette
"It's tough to make
predictions,
especially about the
future.“
Yogi Bera
Slide 4
The Demand Dynamics for Planning
in institutions
Its important to use data to travel to the future…why?
1. Government all over the world is increasingly bothered about tasking
institutions to deliver qualitative education in accordance to set guidelines
and standards.
2. Employers of labour are worried about the quality of entrants into the work
force, most especially having sound employees in order to cope with the
demands of global competition.
3. Globalisation is changing the structure and design of education across
several spheres
4. Advances in technology especially in IT, information spread, and
instructional tools have direct implications for changing instructional
delivery systems.
Slide 5
The Place of Forecasting in Planning
1. Academic Planning proceeds from “what was” to “what is” and “what
should be” in the overall interest of progress and development.
2. Forecasting helps build the bridge between the past and the picture of the
desired future.
3. Forecasting is a prerequisite for planning as the planner must know what is
likely to happen if the present trends continue without policy intervention.
4. A Planner typically puts up a programme for action-a blueprint for
translating policy into practical needs and utility.
Slide 6
Forecasting and Environmental
Scanning
Inputs for forecasting are generally information that are sourced around. This
links Forecasting closely with the concept of Environmental Scanning
1. Analysis of environmental change and the development of institutional
policies to deal with this change are key to an organization's success.
2. Scanning the general environment for trends that affect the organization's
mission is essential to developing an effective strategic plan.
3. Every academic plan becomes real when it takes into account the realities of
the space in which it is implemented. Most institutions have a dedicated unit
for this e.g. The Office of Institutional Analysis (OIA) of the University of
Wisconsin-Madison has the primary institutional responsibility for the
collection and analysis of quantitative and qualitative information on the
institution, its students, its faculty, its programs, its publics, its practices and
its services. The office provides analytic support for the planning, evaluation
and policy initiatives of the Provost and senior leadership and acts as the
institution's reporting agent.
Slide 7
Environmental Scanning-An
Accelrator for Staying Ahead
Environmental Scanning provides a catalyst for staying competitive:
1. a University that establishes an environmental scanning and forecasting
system has benefits of an early warning system to identify trends and
events that, when forecasted, present both threats and opportunities to the
University
2. With early warning, administrators can prepare their response options in
anticipation of changes implied by these trends and events.
3. System will increase management efficiency in dealing with uncertainties
inherent in the future by anticipating change and influencing the future
rather than by simply reacting to it.
4. An environmental scanning system is structured to identify and evaluate
trends, events, and emerging issues important to the institution
Slide 8
Forecasting Defined
1. A forecast is a prediction of what will happen in the future.
2. Forecasting implies the application of a variety of tools to predict
future situations, or results.
3. If we move backward, attempting to predict a previous condition,
the term backcasting can be used.
4. If we know existing time series data points, but there are some
holes, we can estimate the values for those missing points
between known points by a process called interpolation.
Slide 9
The Types of Forecasting
Quantitative forecasting
Qualitative forecasting
Based on numeric data from
several different time periods of
the past that can be assumed
to provide a pattern that allows
us to predict the future.
Relies more heavily on nonnumeric data, the judgments of
specialists, and their variety of
knowledge.
Slide 10
Typical Planning Intervention Data
Source for Forecasting
Goal
Strategy
Sustain and develop
Support the graduate and
select graduate and
professional programs that
professional programs of are already in the top-tier
national and international
distinction.
Offer a resource-rich
training environment for
graduate and professional
students.
Action Plan
Data Needs
Moving on a strong upward
trajectory toward national and
international prominence in faculty
research and scholarly productivity,
including creative work and artistic
performance
Performing at rates comparable to
or above those of peer/aspirant
programs at top-tier public research
universities on commonly
benchmarked metrics (e.g., job
placement, time-to-degree,
licensure pass rates, preparedness
of admittees, extramural
fellowships/funding for students)
Number of research work published,
number of patents created from research,
etc
Increase the recruitment and
retention of graduate and
professional students from
underrepresented groups
% of admitted students from EDS,
number of indigenes on scholarship, etc
(Source: UNIVERSITY OF CONNECTICUT ACADEMIC PLAN 2009 – 2014)
Number of job placement created,
% licensure pass rates,
number of eligibilities for research grants,
etc
Slide 11
A Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 12
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 13
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 14
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 15
Forecasting Horizons
Long Term
Medium Term
5+ years into the future
Program planning, R&D, school location, etc
Principally judgement-based
1 session to 4 years
Aggregate planning, capacity planning, enrolment/employment
forecasts
Mixture of quantitative methods and judgement
Short Term
1 day to 1 year, less than 1 session
Demand forecasting, staffing levels, applications, inventory
requirements
Majorly quantitative methods
Slide 16
Trend Patterns
There are four basic patterns of trendlines.
a.
Constant. : those where there is no net increase or decrease.
b.
Linear : those that show a steady, straight-line increase or
decrease. May go up or down, and the angle may be steep or
shallow..
c.
Exponential :those where the data rises or falls not at a steady
rate, but at an increasing rate.
d.
Polynomial :those best modeled by a polynomial equation. They
may be second-order (quadratic) equations of the form
y = ax2 + bx + c resulting in a parabolic shape or 3rd order.
a.
Moving average :those that uses the average of a number of
past events to even out the likelihood of the next event..
Slide 17
Trend Patterns Illustrated
Constant Trend
Exponential Trend
Linear Trend
2nd Order Polynomial
Trend
Slide 18
Trend Patterns Illustrated
3rd Order Polynomial
Trend
Moving Average
Trends
Slide 19
Designing The Forecast System
Deciding what to forecast
Level of aggregation.
Units of measure.
Choosing the type of forecasting method:
Qualitative methods
Judgment
Quantitative methods
Causal
Time-series
Slide 20
Deciding What to Forecast
Level of Aggregation: The act of clustering several
similar services or programs so that institutions can
obtain more accurate forecasts.
Units of measurement: Determining the standard
calibration for the items in analysis.
Slide 21
Choosing the Type of Forecasting
Techniques
Judgment methods: A type of qualitative method that translates the
opinions of managers, expert opinions, and user surveys into
quantitative estimates.
Causal methods: A type of quantitative method that uses historical
data on independent variables, such as demographic composition,
economic conditions, and competitors’ actions, to predict future
expectations.
Time-series analysis: A statistical approach that relies heavily on
historical demand data to project the future expectations and
recognizes trends and seasonal patterns.
Slide 22
Causal Method: Linear Regression
Causal methods are used when historical data are
available and the relationship between the factor to be
forecasted and other external or internal factors can be
identified.
Linear regression: A causal method in which one
variable (the dependent variable) is related to one or
more independent variables by a linear equation.
Dependent variable: The variable that one wants to forecast.
Independent variables: Variables that are assumed to affect
the dependent variable and thereby “cause” the results
observed in the past.
Slide 23
Causal Methods : Linear Regression
Y
Dependent variable
Estimate of
Y from
regression
equation
Deviation,
or error
{
Regression
equation:
Y = a + bX
Y = dependent variable
X = independent variable
a = Y-intercept of the line
b = slope of the line
Actual
value
of Y
Value of X used
to estimate Y
X
Independent variable
Slide 24
Time Series Method
A time series is a set of observations measured at
successive points in, or over successive periods of time.
The objective of time series methods is to discover a pattern
in the historical data and then extrapolate this pattern into
the future.
The forecast is based solely on past values of the variable
that we are trying to forecast and/or on past forecast errors.
Three time series methods are:
naive forecast
smoothing
trend projection
Slide 25
Assumptions
Assumptions of Time Series Models
There is information about the past;
This information can be quantified in the form of data;
The pattern of the past will continue into the future.
Slide 26
Time Series Methods: Naïve Forecasts
Naive forecast: A time-series method whereby the forecast
for the next period equals the demand for the current
period, or Forecast = Dt
Naive Trend: A time-series method whereby the forecast for
the next period is based on the most recent change between
the last two data points
Slide 27
Smoothing Methods
In cases in which the time series is fairly stable and
has no significant trend, seasonal, or cyclical effects,
one can use smoothing methods to average out the
irregular components of the time series.
Three common smoothing methods are:
Moving averages
Weighted moving averages
Exponential smoothing
Slide 28
Simple Moving Average
Simple moving average method: A time-series method used
to estimate the average of a demand time series by
averaging the demand for the n most recent time periods.
It removes the effects of random fluctuation and is most useful when
demand has no pronounced trend or seasonal influences.
…
The term moving indicates that, as a new observation becomes
available for the time series, it replaces the oldest observation in the
equation, and a new average is computed.
Slide 29
Weighted Moving Average
Weighted moving average method: A time-series method in
which each historical demand in the average can have its
own weight; the more recent observations are typically given
more weight than older observations . The sum of the
weights equals 1.0.
Ft+1 = W1Dt + W2Dt-1 + …+ WnDt-n+1
Slide 30
Exponential Smoothing
Using exponential smoothing, the forecast for the next
period is equal to the forecast for the current period plus
a proportion () of the forecast error in the current period.
The forecast is calculated by:
[the actual value for the current period] + (1- )[the forecasted value for
the current period],
where the smoothing constant, , is a number between 0 and 1.
Exponential smoothing is the most frequently used formal
forecasting method because of its simplicity and the small amount of
data needed to support it.
Slide 31
Trend Adjusted Smoothing
Trend-adjusted exponential smoothing method: The
method for incorporating a trend in an exponentially
smoothed forecast.
With this approach, the estimates for both the average and the
trend are smoothed, requiring two smoothing constants. For
each period, we calculate the average and the trend.
Slide 32
Qualitative Approaches to Forecasting
Delphi Approach
Scenario Writing
Subjective or Interactive Approaches
Slide 33
Qualitative Approaches to Forecasting
Delphi Approach
A panel of experts, each of whom is physically separated
from the others and is anonymous, is asked to respond to
a sequential series of questionnaires.
After each questionnaire, the responses are tabulated and
the information and opinions of the entire group are made
known to each of the other panel members so that they
may revise their previous forecast response.
The process continues until some degree of consensus is
achieved.
Slide 34
Qualitative Approaches to Forecasting
Scenario Writing
Scenario writing consists of developing a conceptual
scenario of the future based on a well defined set of
assumptions.
After several different scenarios have been developed, the
decision maker determines which is most likely to occur in
the future and makes decisions accordingly.
Slide 35
Qualitative Approaches to Forecasting
Subjective or Interactive Approaches
These techniques are often used by committees or panels
seeking to develop new ideas or solve complex problems.
They often involve "brainstorming sessions".
It is important in such sessions that any ideas or opinions be
permitted to be presented without regard to its relevancy and
without fear of criticism.
Slide 36
Tools for Trend Analysis
1.
2.
3.
4.
Formulas
Microsoft Excel Charts
JavaScripts
Custom Apps [SuperSMITH Visual, Question Pro]
Slide 37
Hands on Exercise on Forecasting
and Trend Analysis
Simple Forecasting with
1. Excel Chart Tools
2. Simple Moving Averages
Slide 38
Questions and Answers
Slide 39
Thank you
Further questions could be directed to
[email protected]
Forecasting
and Trend
Analysis
Paper presented at The 2014 Training Workshop of Committee of
Directors of Academic Planning of Nigerian Universities
By
Samson Akinyosoye
October 2014
Slide 2
Learning Objectives
This lecture will have the following broad objectives:
1.
2.
3.
4.
To understand the role of data in planning
To highlight the role of data in forecasting
To identify different data presentation for forecasting
To discuss simple forecasting and trend analysis
methods
5. To introduce participants to simple tools for forecasting
and trend analysis
Slide 3
Opening Vignette
"It's tough to make
predictions,
especially about the
future.“
Yogi Bera
Slide 4
The Demand Dynamics for Planning
in institutions
Its important to use data to travel to the future…why?
1. Government all over the world is increasingly bothered about tasking
institutions to deliver qualitative education in accordance to set guidelines
and standards.
2. Employers of labour are worried about the quality of entrants into the work
force, most especially having sound employees in order to cope with the
demands of global competition.
3. Globalisation is changing the structure and design of education across
several spheres
4. Advances in technology especially in IT, information spread, and
instructional tools have direct implications for changing instructional
delivery systems.
Slide 5
The Place of Forecasting in Planning
1. Academic Planning proceeds from “what was” to “what is” and “what
should be” in the overall interest of progress and development.
2. Forecasting helps build the bridge between the past and the picture of the
desired future.
3. Forecasting is a prerequisite for planning as the planner must know what is
likely to happen if the present trends continue without policy intervention.
4. A Planner typically puts up a programme for action-a blueprint for
translating policy into practical needs and utility.
Slide 6
Forecasting and Environmental
Scanning
Inputs for forecasting are generally information that are sourced around. This
links Forecasting closely with the concept of Environmental Scanning
1. Analysis of environmental change and the development of institutional
policies to deal with this change are key to an organization's success.
2. Scanning the general environment for trends that affect the organization's
mission is essential to developing an effective strategic plan.
3. Every academic plan becomes real when it takes into account the realities of
the space in which it is implemented. Most institutions have a dedicated unit
for this e.g. The Office of Institutional Analysis (OIA) of the University of
Wisconsin-Madison has the primary institutional responsibility for the
collection and analysis of quantitative and qualitative information on the
institution, its students, its faculty, its programs, its publics, its practices and
its services. The office provides analytic support for the planning, evaluation
and policy initiatives of the Provost and senior leadership and acts as the
institution's reporting agent.
Slide 7
Environmental Scanning-An
Accelrator for Staying Ahead
Environmental Scanning provides a catalyst for staying competitive:
1. a University that establishes an environmental scanning and forecasting
system has benefits of an early warning system to identify trends and
events that, when forecasted, present both threats and opportunities to the
University
2. With early warning, administrators can prepare their response options in
anticipation of changes implied by these trends and events.
3. System will increase management efficiency in dealing with uncertainties
inherent in the future by anticipating change and influencing the future
rather than by simply reacting to it.
4. An environmental scanning system is structured to identify and evaluate
trends, events, and emerging issues important to the institution
Slide 8
Forecasting Defined
1. A forecast is a prediction of what will happen in the future.
2. Forecasting implies the application of a variety of tools to predict
future situations, or results.
3. If we move backward, attempting to predict a previous condition,
the term backcasting can be used.
4. If we know existing time series data points, but there are some
holes, we can estimate the values for those missing points
between known points by a process called interpolation.
Slide 9
The Types of Forecasting
Quantitative forecasting
Qualitative forecasting
Based on numeric data from
several different time periods of
the past that can be assumed
to provide a pattern that allows
us to predict the future.
Relies more heavily on nonnumeric data, the judgments of
specialists, and their variety of
knowledge.
Slide 10
Typical Planning Intervention Data
Source for Forecasting
Goal
Strategy
Sustain and develop
Support the graduate and
select graduate and
professional programs that
professional programs of are already in the top-tier
national and international
distinction.
Offer a resource-rich
training environment for
graduate and professional
students.
Action Plan
Data Needs
Moving on a strong upward
trajectory toward national and
international prominence in faculty
research and scholarly productivity,
including creative work and artistic
performance
Performing at rates comparable to
or above those of peer/aspirant
programs at top-tier public research
universities on commonly
benchmarked metrics (e.g., job
placement, time-to-degree,
licensure pass rates, preparedness
of admittees, extramural
fellowships/funding for students)
Number of research work published,
number of patents created from research,
etc
Increase the recruitment and
retention of graduate and
professional students from
underrepresented groups
% of admitted students from EDS,
number of indigenes on scholarship, etc
(Source: UNIVERSITY OF CONNECTICUT ACADEMIC PLAN 2009 – 2014)
Number of job placement created,
% licensure pass rates,
number of eligibilities for research grants,
etc
Slide 11
A Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 12
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 13
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 14
A Further Look at Planning Data
(Source: UNIVERSITY OF WISCONSIN DATA DIGEST)
Slide 15
Forecasting Horizons
Long Term
Medium Term
5+ years into the future
Program planning, R&D, school location, etc
Principally judgement-based
1 session to 4 years
Aggregate planning, capacity planning, enrolment/employment
forecasts
Mixture of quantitative methods and judgement
Short Term
1 day to 1 year, less than 1 session
Demand forecasting, staffing levels, applications, inventory
requirements
Majorly quantitative methods
Slide 16
Trend Patterns
There are four basic patterns of trendlines.
a.
Constant. : those where there is no net increase or decrease.
b.
Linear : those that show a steady, straight-line increase or
decrease. May go up or down, and the angle may be steep or
shallow..
c.
Exponential :those where the data rises or falls not at a steady
rate, but at an increasing rate.
d.
Polynomial :those best modeled by a polynomial equation. They
may be second-order (quadratic) equations of the form
y = ax2 + bx + c resulting in a parabolic shape or 3rd order.
a.
Moving average :those that uses the average of a number of
past events to even out the likelihood of the next event..
Slide 17
Trend Patterns Illustrated
Constant Trend
Exponential Trend
Linear Trend
2nd Order Polynomial
Trend
Slide 18
Trend Patterns Illustrated
3rd Order Polynomial
Trend
Moving Average
Trends
Slide 19
Designing The Forecast System
Deciding what to forecast
Level of aggregation.
Units of measure.
Choosing the type of forecasting method:
Qualitative methods
Judgment
Quantitative methods
Causal
Time-series
Slide 20
Deciding What to Forecast
Level of Aggregation: The act of clustering several
similar services or programs so that institutions can
obtain more accurate forecasts.
Units of measurement: Determining the standard
calibration for the items in analysis.
Slide 21
Choosing the Type of Forecasting
Techniques
Judgment methods: A type of qualitative method that translates the
opinions of managers, expert opinions, and user surveys into
quantitative estimates.
Causal methods: A type of quantitative method that uses historical
data on independent variables, such as demographic composition,
economic conditions, and competitors’ actions, to predict future
expectations.
Time-series analysis: A statistical approach that relies heavily on
historical demand data to project the future expectations and
recognizes trends and seasonal patterns.
Slide 22
Causal Method: Linear Regression
Causal methods are used when historical data are
available and the relationship between the factor to be
forecasted and other external or internal factors can be
identified.
Linear regression: A causal method in which one
variable (the dependent variable) is related to one or
more independent variables by a linear equation.
Dependent variable: The variable that one wants to forecast.
Independent variables: Variables that are assumed to affect
the dependent variable and thereby “cause” the results
observed in the past.
Slide 23
Causal Methods : Linear Regression
Y
Dependent variable
Estimate of
Y from
regression
equation
Deviation,
or error
{
Regression
equation:
Y = a + bX
Y = dependent variable
X = independent variable
a = Y-intercept of the line
b = slope of the line
Actual
value
of Y
Value of X used
to estimate Y
X
Independent variable
Slide 24
Time Series Method
A time series is a set of observations measured at
successive points in, or over successive periods of time.
The objective of time series methods is to discover a pattern
in the historical data and then extrapolate this pattern into
the future.
The forecast is based solely on past values of the variable
that we are trying to forecast and/or on past forecast errors.
Three time series methods are:
naive forecast
smoothing
trend projection
Slide 25
Assumptions
Assumptions of Time Series Models
There is information about the past;
This information can be quantified in the form of data;
The pattern of the past will continue into the future.
Slide 26
Time Series Methods: Naïve Forecasts
Naive forecast: A time-series method whereby the forecast
for the next period equals the demand for the current
period, or Forecast = Dt
Naive Trend: A time-series method whereby the forecast for
the next period is based on the most recent change between
the last two data points
Slide 27
Smoothing Methods
In cases in which the time series is fairly stable and
has no significant trend, seasonal, or cyclical effects,
one can use smoothing methods to average out the
irregular components of the time series.
Three common smoothing methods are:
Moving averages
Weighted moving averages
Exponential smoothing
Slide 28
Simple Moving Average
Simple moving average method: A time-series method used
to estimate the average of a demand time series by
averaging the demand for the n most recent time periods.
It removes the effects of random fluctuation and is most useful when
demand has no pronounced trend or seasonal influences.
…
The term moving indicates that, as a new observation becomes
available for the time series, it replaces the oldest observation in the
equation, and a new average is computed.
Slide 29
Weighted Moving Average
Weighted moving average method: A time-series method in
which each historical demand in the average can have its
own weight; the more recent observations are typically given
more weight than older observations . The sum of the
weights equals 1.0.
Ft+1 = W1Dt + W2Dt-1 + …+ WnDt-n+1
Slide 30
Exponential Smoothing
Using exponential smoothing, the forecast for the next
period is equal to the forecast for the current period plus
a proportion () of the forecast error in the current period.
The forecast is calculated by:
[the actual value for the current period] + (1- )[the forecasted value for
the current period],
where the smoothing constant, , is a number between 0 and 1.
Exponential smoothing is the most frequently used formal
forecasting method because of its simplicity and the small amount of
data needed to support it.
Slide 31
Trend Adjusted Smoothing
Trend-adjusted exponential smoothing method: The
method for incorporating a trend in an exponentially
smoothed forecast.
With this approach, the estimates for both the average and the
trend are smoothed, requiring two smoothing constants. For
each period, we calculate the average and the trend.
Slide 32
Qualitative Approaches to Forecasting
Delphi Approach
Scenario Writing
Subjective or Interactive Approaches
Slide 33
Qualitative Approaches to Forecasting
Delphi Approach
A panel of experts, each of whom is physically separated
from the others and is anonymous, is asked to respond to
a sequential series of questionnaires.
After each questionnaire, the responses are tabulated and
the information and opinions of the entire group are made
known to each of the other panel members so that they
may revise their previous forecast response.
The process continues until some degree of consensus is
achieved.
Slide 34
Qualitative Approaches to Forecasting
Scenario Writing
Scenario writing consists of developing a conceptual
scenario of the future based on a well defined set of
assumptions.
After several different scenarios have been developed, the
decision maker determines which is most likely to occur in
the future and makes decisions accordingly.
Slide 35
Qualitative Approaches to Forecasting
Subjective or Interactive Approaches
These techniques are often used by committees or panels
seeking to develop new ideas or solve complex problems.
They often involve "brainstorming sessions".
It is important in such sessions that any ideas or opinions be
permitted to be presented without regard to its relevancy and
without fear of criticism.
Slide 36
Tools for Trend Analysis
1.
2.
3.
4.
Formulas
Microsoft Excel Charts
JavaScripts
Custom Apps [SuperSMITH Visual, Question Pro]
Slide 37
Hands on Exercise on Forecasting
and Trend Analysis
Simple Forecasting with
1. Excel Chart Tools
2. Simple Moving Averages
Slide 38
Questions and Answers
Slide 39
Thank you
Further questions could be directed to
[email protected]