Lecture_2_FC_2011_Give - Forecasting Forecasting...

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Forecasting Forecasting SUMMARIZING -Today Forecasting: 1. Long term : not much known, numerical data not available. Use of judgment- technological forecasting methods 2. Short Term: Forecasting – quantitative models if data allows. 3. Quantitative: Explanatory F= f (X 1 , X 2 , X 3 ….) Time series: F T = f { A T-1 , A T-2 , A T-3 , …} 4. Cross sectional data vs Time series data. 5. Mathematical method for seasonality 6. Error Analysis: Residuals and error minimization (MSE & MAPE)
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• Assumes causal system past ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate for groups vs. individuals • Forecast accuracy decreases as time horizon increases • ‘Ceteris Paribus’ assumption in Time series. I see that you will get an A this semester. 1. Averaging methods: Moving Averages smoothing Weighted Moving Averages smoothing Exponential Smoothing 2. Trend analysis : Linear trend equation, Trend adjusted Exponential smoothing 2. Causal methods: Simple linear regression 3. Time Series Decomposition seasonality Forecasting _ Anchor Forecasting _ Anchor
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SUMMARIZING: Product Demand Charted over 4 Years with Trend and Seasonality Year 1 Year 2 Year 3 Year 4 Seasonal peaks Trend component Actual demand line Average demand over four years Demand for product or service Random variation Naive Approach • Assumes demand in next period is the same as demand in most recent period – e.g., If May sales were 48, then June sales will be 48 • Sometimes cost effective & efficient © 1995 Coel Corp.
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MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Equation MA MA n n = Demand in Demand in Previous Previous Periods Periods Moving Average Method You’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 2003 using a 3 - period moving average. 1998 4 1999 6 2000 5 2001 3 2002 7 Moving Average Example
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Moving Average Solution Time Response Y i Moving Total (n=3) Average 1998 4 NA 1999 6 2000 5 2001 3 4+6+5=15 15/3 = 5 2002 7 2003 Moving Average Solution Y i 6+5+3=14 14/3=4 2/3
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Moving Average Solution Time Response Y i Moving Total (n=3) Average 1998 4 NA 1999 6 2000 5 2001 3 4+6+5=15 15/3=5.0 2002 7 6+5+3=14 14/3=4.7 2003 5+3+7=15 95 96 97 98 99 00 Year Sales 2 4 6 8 Actual Forecast Moving Average Graph
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Time Time -Series Methods Series Methods Simple Moving Averages Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — ||| 0 5 10 15 20 25 30 Actual patient arrivals Patient arrivals Time Time -Series Methods Series Methods Simple Moving Averages Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week 0 5 10 15 20 25 30
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Time Time -Series Methods Series Methods Simple Moving Averages Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week ||| 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals Time Time -Series Methods Series Methods Simple Moving Averages Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411
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Time Time -Series Methods Series Methods Simple Moving Averages
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This note was uploaded on 03/03/2011 for the course MARKETING 101 taught by Professor Singh during the Spring '11 term at Management Development Institute.

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Lecture_2_FC_2011_Give - Forecasting Forecasting...

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