2 - Forecasting and Estimation Flashcards
What is a forecast?
Statements about future events, particularly future values of economic variables, based on past observations and theoretically grounded, objective methods. Forecasts apply particularly to variables that cannot or can barely be influenced by the instance in charge of forecasting.
What are the implications of forecasting errors?
faulty forecasts enable bad decisions
What forecasting errors are there?
over-forecast
- e.g. planning too much capacity or expecting too high cost
(glass half full)
under-forecast:
- turning down valuable customers or spending too much time or money
(glass more than full)
What kinds of forecast data are there?
Transaction data
Controls data
Historical forecast data
Auxiliary data
Kinds of forecast data
Transaction data
- units of capacity sold per product
- number of appointments serviced on time
Kinds of forecast data
Controls data
- appointment slots offered
- number of employees on shift
How much did we actually put on the market?
Kinds of forecast data
Historical forecast data
- expected service time
- forecasted demand
What did we estimate in the past?
Kinds of forecast data
Auxiliary data
- currency or tax data
- demographic information
- weather data
Additional data from other sources
Levels of aggregation
Design decision: How much to aggregate?
detailed information for finely tuned controls
vs.
few observations and small numbers
Examples:
Forecast appointment requests per day and service point?
Forecast appointment requests per week, expecting possible shifts?
Forecast travel times per month or day of the week?
-> aggregation depends on the data we have and the target we have
Forecasting methods - overview
What types of methods are there?
Ad-hoc or structural methods
time-series methods
Forecasting methods - overview
Ad-hoc or structural methods
- purely descriptive, based on historical observations of trends
- identify level, trend, seasonality
- e.g. m-period moving average, exponential smoothing
-> Returns a function to predict the next value from historical observations
Forecasting methods - overview
Time-series methods
- model an underlying dynamic system that generates data over time
- e.g. auto-regressive process, ARIMA
-> returns a model to predict the next value as resulting from the process - needs an estimation method to find the best model and its parameters
The simplest ad-hoc method
Simple moving average
compute the new forecast results by equally weighting the past n values
- every new observation enters the forecast
- the forecast evens out for all future instances
- strongly depends on the chosen n
Another ad-hoc method
Exponential smoothing
Parameter Alpha in [0 and 1] determines the weight of new observations
Alpha > 0,5: more emphasis on the latest observation
-> adapts quickly but is volatile
Alpha ≤ 0,5: more emphasis on history than on the latest observation
-> very stable but takes long to adapt
Alpha = 1: the naïve forecast - forecasts the value of the latest observation
-> a frequent benchmark
Time-series methods
Time-series forecasting in three steps
- make a hypothesis about the process generating the data
- estimate process parameters
- apply the best forecasting method for the model
In contrast to structural forecasting, time-series methods can exploit correlations in the data
