Demand Forecasting Time Series Models Professor Stephen R
- Slides: 36
Demand Forecasting: Time Series Models Professor Stephen R. Lawrence College of Business and Administration University of Colorado Boulder, CO 80309 -0419
Forecasting Horizons o Long Term · 5+ years into the future · R&D, plant location, product planning · Principally judgement-based o Medium Term · 1 season to 2 years · Aggregate planning, capacity planning, sales forecasts · Mixture of quantitative methods and judgement o Short Term · 1 day to 1 year, less than 1 season · Demand forecasting, staffing levels, purchasing, inventory levels · Quantitative methods
Short Term Forecasting: Needs and Uses o Scheduling existing resources · How many employees do we need and when? · How much product should we make in anticipation of demand? o Acquiring additional resources · When are we going to run out of capacity? · How many more people will we need? · How large will our back-orders be? o Determining what resources are needed · What kind of machines will we require? · Which services are growing in demand? declining? · What kind of people should we be hiring?
Types of Forecasting Models o Types of Forecasts · Qualitative --- based on experience, judgement, knowledge; · Quantitative --- based on data, statistics; o Methods of Forecasting · Naive Methods --- eye-balling the numbers; · Formal Methods --- systematically reduce forecasting errors; �time series models (e. g. exponential smoothing); �causal models (e. g. regression). · Focus here on Time Series Models o 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.
Forecasting Examples o Examples from student projects: · · o Demand for tellers in a bank; Traffic on major communication switch; Demand for liquor in bar; Demand for frozen foods in local grocery warehouse. Example from Industry: American Hospital Supply Corp. · · · 70, 000 items; 25 stocking locations; Store 3 years of data (63 million data points); Update forecasts monthly; 21 million forecast updates per year.
Simple Moving Average o Forecast Ft is average of n previous observations or actuals Dt : Note that the n past observations are equally weighted. o Issues with moving average forecasts: o · · All n past observations treated equally; Observations older than n are not included at all; Requires that n past observations be retained; Problem when 1000's of items are being forecast.
Simple Moving Average Include n most recent observations o Weight equally o Ignore older observations o weight 1/n n . . . 3 2 1 today
Moving Average n=3
Example: Moving Average Forecasting
Exponential Smoothing I Include all past observations o Weight recent observations much more heavily than very old observations: o weight Decreasing weight given to older observations today
Exponential Smoothing I Include all past observations o Weight recent observations much more heavily than very old observations: o weight Decreasing weight given to older observations today
Exponential Smoothing I Include all past observations o Weight recent observations much more heavily than very old observations: o weight Decreasing weight given to older observations today
Exponential Smoothing I Include all past observations o Weight recent observations much more heavily than very old observations: o weight Decreasing weight given to older observations today
Exponential Smoothing: Concept Include all past observations o Weight recent observations much more heavily than very old observations: o weight Decreasing weight given to older observations today
Exponential Smoothing: Math
Exponential Smoothing: Math
Exponential Smoothing: Math Thus, new forecast is weighted sum of old forecast and actual demand o Notes: o · Only 2 values (Dt and Ft-1 ) are required, compared with n for moving average · Parameter a determined empirically (whatever works best) · Rule of thumb: < 0. 5 · Typically, = 0. 2 or = 0. 3 work well o Forecast for k periods into future is:
Exponential Smoothing = 0. 2
Example: Exponential Smoothing
Complicating Factors o Simple Exponential Smoothing works well with data that is “moving sideways” (stationary) o Must be adapted for data series which exhibit a definite trend o Must be further adapted for data series which exhibit seasonal patterns
Holt’s Method: Double Exponential Smoothing o What happens when there is a definite trend? A trendy clothing boutique has had the following sales over the past 6 months: 1 2 3 4 5 6 510 512 528 530 542 552 Actual Demand Forecast Month
Holt’s Method: Double Exponential Smoothing o Ideas behind smoothing with trend: · ``De-trend'' time-series by separating base from trend effects · Smooth base in usual manner using · Smooth trend forecasts in usual manner using o Smooth the base forecast Bt o Smooth the trend forecast Tt o Forecast k periods into future Ft+k with base and trend
ES with Trend = 0. 2, = 0. 4
Example: Exponential Smoothing with Trend
Winter’s Method: Exponential Smoothing w/ Trend and Seasonality o Ideas behind smoothing with trend and seasonality: · “De-trend’: and “de-seasonalize”time-series by separating base from trend and seasonality effects · Smooth base in usual manner using · Smooth trend forecasts in usual manner using · Smooth seasonality forecasts using g o Assume m seasons in a cycle · · 12 months in a year 4 quarters in a month 3 months in a quarter et cetera
Winter’s Method: Exponential Smoothing w/ Trend and Seasonality o Smooth the base forecast Bt o Smooth the trend forecast Tt o Smooth the seasonality forecast St
Winter’s Method: Exponential Smoothing w/ Trend and Seasonality o Forecast Ft with trend and seasonality o Smooth the trend forecast Tt o Smooth the seasonality forecast St
ES with Trend and Seasonality = 0. 2, = 0. 4, g = 0. 6
Example: Exponential Smoothing with Trend and Seasonality
Forecasting Performance How good is the forecast? o Mean Forecast Error (MFE or Bias): Measures average deviation of forecast from actuals. o Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals. o Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast. o Standard Squared Error (MSE): Measures variance of forecast error
Forecasting Performance Measures
Mean Forecast Error (MFE or Bias) Want MFE to be as close to zero as possible -minimum bias o A large positive (negative) MFE means that the forecast is undershooting (overshooting) the actual observations o Note that zero MFE does not imply that forecasts are perfect (no error) -- only that mean is “on target” o Also called forecast BIAS o
Mean Absolute Deviation (MAD) Measures absolute error o Positive and negative errors thus do not cancel out (as with MFE) o Want MAD to be as small as possible o No way to know if MAD error is large or small in relation to the actual data o
Mean Absolute Percentage Error (MAPE) Same as MAD, except. . . o Measures deviation as a percentage of actual data o
Mean Squared Error (MSE) Measures squared forecast error -- error variance o Recognizes that large errors are disproportionately more “expensive” than small errors o But is not as easily interpreted as MAD, MAPE -- not as intuitive o
Fortunately, there is software. . .
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- Contoh analisis time series laporan keuangan
- Centered moving average example
- Time series forecasting
- Promotion from assistant to associate professor
- Types of forecasting models
- Statistical forecasting models
- Hotel demand forecasting
- Statistical methods of demand forecasting
- Chain ratio method of demand forecasting
- Importance of forcasting
- Statistical methods of demand forecasting
- Demand estimation and forecasting
- Collaborative planning forecasting and replenishment
- Demand estimation and forecasting
- Forecasting and demand measurement in marketing
- Market buildup method
- Marketing research and forecasting demand
- Forecasting demand for autonomous vehicles
- Marketing approach to demand measurement
- Factors affecting hr demand forecasting
- Demand forecasting objectives
- Forecasting advantages
- Forecasting in operations management
- Forecasting and demand measurement in marketing
- What is demand forecasting and estimation
- Collecting information and forecasting demand
- Dynamic traffic assignment
- Demand forecasting introduction
- Demand forecasting objectives
- Limitations of demand forecasting
- Simultaneous equation method in demand forecasting
- Gathering information and measuring market demand
- Conducting marketing research and forecasting demand
- Conducting marketing research and forecasting demand
- Demand forecasting
- Chain ratio method of demand forecasting