FORECASTING MODEL

FORECASTING MODELRAJ GAUR214213003Introduction to ForecastingWhat is forecasting?Primary Function is to Predict the Future using (time series related or other) data we have in handWhy are we interested?Affects the decisions we make todayWhere is forecasting used forecast demand for products and servicesforecast availability/need for manpowerforecast inventory and materiel needs daily

What Makes a Good Forecast?It should be timelyIt should be as accurate as possibleIt should be reliableIt should be in meaningful unitsIt should be presented in writingThe method should be easy to use and understand in most cases.

Types of Forecasting MethodsForecasting methods are classified into two groups:

Types of Forecasting ModelsQualitative methods judgmental methodsForecasts generated subjectively by the forecasterEducated guesses Quantitative methods based on mathematical modelingForecasts generated through mathematical modeling

Qualitative Methods

Quantitative MethodsTime Series Models:Assumes information needed to generate a forecast is contained in a time series of dataAssumes the future will follow same patterns as the pastCausal Models or Associative ModelsExplores cause-and-effect relationshipsUses leading indicators to predict the futureHousing starts and appliance sales

Time Series ModelsForecaster looks for data patterns as Data = historic pattern + random variationHistoric pattern to be forecasted: Level (long-term average) data fluctuates around a constant meanTrend data exhibits an increasing or decreasing pattern Seasonality any pattern that regularly repeats itself and is of a constant lengthCycle patterns created by economic fluctuations Random Variation cannot be predicted

Time Series Patterns

Moving AveragesIn words: the arithmetic average of the n most recent observations. For a one-step-ahead forecast: Ft = (1/N) (Dt - 1 + Dt - 2 + . . . + Dt - n )

example3 month MA: (oct+nov+dec)/3=258.336 month MA: (jul+aug++dec)/6=249.3312 month MA: (Jan+feb++dec)/12=205.33MONTHDemandMonthDemandJanuary89July223February57August286March144September212April221October275May177November188June280December312Summary of Moving AveragesAdvantages of Moving Average MethodEasily understoodEasily computedProvides stable forecastsDisadvantages of Moving Average MethodRequires saving lots of past data points: at least the N periods used in the moving average computationLags behind a trendIgnores complex relationships in data

Weighted Moving Averageconsists of computing a weighted average of the most recent n data values for the series and using this weighted average for forecasting the value of the time series for the next period. The more recent observations are typically given more weight than older observations. For convenience, the weights usually sum to 1.Weighted Moving AverageAll weights must add to 100% or 1.00 e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)

Exponential SmoothingThe 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.Using exponential smoothing, the forecast is calculated by: Ft+1= Yt + (1- )Ft where: is the smoothing constant (a number between 0 and 1)Ft is the forecast for period tFt +1 is the forecast for period t+1Yt is the actual data value for period t

Exponential Smoothing

F11 = 0.1 * Y10 + .9 F10 = .1 *130 + .9 * 115.4099 = 116.87 Effect of value on the ForecastSmall values of means that the forecasted value will be stable (show low variabilityLow increases the lag of the forecast to the actual data if a trend is presentLarge values of mean that the forecast will more closely track the actual time series

Forecasting TrendBasic forecasting models for trends compensate for the lagging that would otherwise occurOne model, trend-adjusted exponential smoothing uses a three step processStep 1 - Smoothing the level of the series

Step 2 Smoothing the trend

Forecast including the trend

Causal Models

Often, leading indicators can help to predict changes in future demand e.g. housing startsCausal models establish a cause-and-effect relationship between independent and dependent variablesA common tool of causal modeling is linear regression:Additional related variables may require multiple regression modelingLinear Regression

Identify dependent (y) and independent (x) variablesSolve for the slope of the line

Solve for the y intercept

Develop your equation for the trend line Y=a + bX

Measuring Forecasting AccuracyMean Absolute Deviation (MAD)measures the total error in a forecast without regard to sign

Cumulative Forecast Error (CFE)Measures any bias in the forecast

Mean Square Error (MSE)Penalizes larger errors

Sheet1TypeCharacteristicsStrengthsWeaknessesExecutive opinionA group of managers meet & come up with a forecastGood for strategic or new-product forecastingOne person's opinion can dominate the forecastMarket researchUses surveys & interviews to identify customer preferencesGood determinant of customer preferencesIt can be difficult to develop a good questionnaireDelphi methodSeeks to develop a consensus among a group of expertsExcellent for forecasting long-term product demand, technological changes, and scientific advancesTime consuming to develop

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Sheet1Robert's Drugs=0.1Week (t)SalestFt111021151103125110.54120111.955125112.7556120113.97957130114.581558115116.1233959110116.011055510130115.4099499511MSE0

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