Man3504 Test 2 Set 4

Front 1

is the process of projecting the values of one or more variables into the future.

Back 1

forecasting

Front 2

is the length of time on which a forecast is based. This spans from short-range forecasts with a planning horizon of under 3 months to long-range forecasts of 1 to 10 years.

Back 2

planning horizon

Front 3

is a set of observations measured at successive points in time or over successive periods of time. A time series pattern may have one or more of the following five characteristics:
Trend
Seasonal
Cyclical
Random Variation
Irregular (one time) Variation

Back 3

a time series

Front 4

are characterized by repeatable periods of ups and downs over short periods of time

Back 4

seasonal patterns

Front 5

are semi-regular patterns in a data series that take place over long periods of time. Usually related to business cycles

Back 5

cyclical patterns

Front 6

(sometimes called noise) is the unexplained deviation of a time series from a predictable pattern, such as a trend, seasonal, or cyclical pattern. Because of these random variations, forecasts are never 100 percent accurate

Back 6

random variation

Front 7

is one-time variation that is explainable. For example, a hurricane can cause a surge in demand for building materials, food, and water.

Back 7

irregular variation

Front 8

is the difference between the observed value of the time series and the forecast,

Back 8

Forecast error (et)

Front 9

compares the size of the error to the actual demand

Back 9

forecast percentage error

Front 10

A major difference between MSE and MAD is that MSE is influenced much more by -------------------- (because errors are squared).

Back 10

large forecasts errors than by small errors

Front 11

is different in that the measurement scale factor is eliminated by dividing the absolute error by the time-series value data. This makes the measure easier to interpret.

Back 11

mape

Front 12

relies upon opinions and expertise of people in developing forecasts.
Surveys
Delphi Method
Others

Back 12

judgemental forecasting

Front 13

When no ----------------- is available, only judgmental forecasting is possible.

Back 13

historical data

Front 14

consists of forecasting by expert opinion by gathering judgments and opinions of key personnel based on their experience and knowledge of the situation.

Back 14

the delphi approach

Front 15

Ability to incorporate unusual or one-time events, and
The difficulty of obtaining the data necessary for quantitative techniques
Long range forecasting for strategic planning

Back 15

major reasons for using judgmental forecasting

Front 16

are based on developing relationships of demand with other factors
Regression-based
Ski resort demand with snowfall estimates
Lumber demands with housing building permits

Back 16

causal models

Front 17

is based on the assumption that the future will be an extrapolation of the past.
Averages, Smoothing techniques, regression

Back 17

time series

Front 18

All of the simple models are weighted averages (Note that the sum of the weights must be equal to one)
Difference is in the assignment of weights
Higher weights on more recent data make a forecast more responsive
Higher weights on older data make a forecast more stable

Back 18

simple models

Front 19

A forecast which is too responsive will tend to --------- recent data.
This is especially worrisome if the recent data is anomalous for some reason

Back 19

exaggerate

Front 20

A statistical model that defines a relationship between a single dependent variable and one or more independent variables, all of which are numerical.

Back 20

regression analysis