Overview of Methods

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Overview of Methods
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Quantitative Techniques

• Moving Average
• Trend Analysis
• Exponential Smoothing
• ARIMA models
• Econometric models

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Moving Average

• A simple average of the previous X months/years
• A six-month moving average forecast is an average
  of the previous six months

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I always avoid prophesying beforehand because it
much better to prophesy after the event has
already taken place.

• Winston Churchill

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Moving Average When To Use

• Extremely noisy or little data
• Time constraint
• Degree of accuracy not important

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Moving Average - Advantages

• Extremely simple
• Easy to implement

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Moving Average - Disadvantages

• Not accurate slow adjustment to changes in data
• Misses turning points
• All history is created equal

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Moving Average Example
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Trend Regression

• A straight ( or curved) line drawn through
  historical data
• taking a ruler through your data

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The best qualification of a prophet is to have a
good memory.

• Marquis of Halifax

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Trend Regression When To Use

• Steady rise or decline in data
• Time or software constraint
• Need easy explanation
• Little data

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Trend Regression - Advantages

• Very simple
• Can be done in Excel

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Trend Regression Disadvantages

• Assumes future is exactly like past (prices,
  economy, etc.)
• All history is created equal
• One bad data point can greatly affect forecast

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Trend Regression Example
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Exponential Smoothing

• Simple
• Double (Brown) or Holt
• Winters

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A good forecaster is not smarter than everyone
else, he merely has his ignorance better
organized.

• C. W. J. Granger

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Simple Exponential Smoothing

• Weighted average of past values with
  exponentially decreasing weights
• Forecast this month equals last months forecast
  plus a proportion of the forecast error last month

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Simple Exponential Smoothing When To Use

• Stationary data with no trend or seasonality

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Double (Brown) or Holt Exponential Smoothing

• Smooth the smoothed data with a weighted average
  of past values with exponentially decreasing
  weights
• Changes linearly with time (like linear
  regression) with recent data given more weight

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Double (Brown) or Holt Exponential Smoothing
When to Use

• Data with a trend but no seasonality

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Winters Exponential Smoothing

• Deseasonalize data, then find trend, then smooth

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Winters Exponential Smoothing When to Use

• Data with trend and seasonality

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Exponential Smoothing Advantages

• Somewhat simple
• Recent data given more weight
• Fairly good accuracy for short-term forecasts
• Software can automate process

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Exponential Smoothing - Disadvantages

• Requires forecasting software
• Bad data in recent month can cause great error in
  forecast
• Less accurate for medium to long-term forecasts
• Assumes history is like (recent) history

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Exponential Smoothing Example
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ARIMA (Box-Jenkins) Models

• AutoRegressive Integrative Moving Average
• Autoregressive future values depend on previous
  values of the data
• Moving average future values depend on previous
  values of the errors
• Integrated refers to differencing the data

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An unsophisticated forecaster uses statistics as
a drunken man uses lamp-posts for support
rather than illumination

• - after Andrew Lang

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ARIMA (Box-Jenkins) Models When to Use

• Stable data that has regular correlations

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ARIMA ( Box-Jenkins) Models - Advantages

• Outperforms exponential smoothing on homogenous
  and stable data
• Software can automate
• Sounds impressive

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ARIMA (Box-Jenkins) Models - Disadvantages

• Requires software
• Needs a minimum of 40 data points
• Complicated to understand

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ARIMA (Box-Jenkins) Models Example
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Econometric Models

• Relates data series to explanatory variables
• Economists build demand models which relate
• Price, competition, income, population, etc.

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An economist is an expert who will know tomorrow
why the things he predicted yesterday didnt
happen today.

• Evan Esar

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Econometric Models When to Use

• Important to understand market
• Influences on product demand are changing
• Historically more acceptable in regulation

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Econometric Models - Advantages

• Can give price elasticity
• Formally integrates economic impact
• Permits varied assumptions, i.e., what if?
• Forces you to make assumptions explicit
• Methods to deal with short time

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Econometric Models - Disadvantages

• Large data gathering
• Expertise to build
• Requires forecasts of explanatory variables
• Not always best forecasting technique

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Econometric Models Example
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Res. DA Model

• DA calls per person 4.49-0.18Price
  1.04Income per person0.00016Timetrend

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Summary

• Graph data
• Choose appropriate technique for
• Output
• Time
• Data
• Know advantages and disadvantages