Ch. 11 Forecasting

Question 1

• A process for predicting the future
• Forecasts become less accurate over time.
• Forecasts for aggregates are more accurate than forecasts for individual items.
• Forecasts are often used as a motivator or strategic objective.
• Forecasts should be developed using several different methods.
• A good forecast should consist of both a point as well as a range.

Answer 1

Forecasting Principles

Question 2

What are the three types of Forecasting approaching?

Answer 2

1. Judgmental
2. Time Series
3. Regression

Question 3

A set of data points recorded over successive time periods
Projects past trends into the future
Chronologically ordered sequence of data values for one variable

Answer 3

Time Series

Question 4

Examples of Time Series

Answer 4

• month unemployment rate for the period 2010 to 2019

- reports of the closing value Dow Jones Industrial average

Question 5

technique that analyzes the past behavior of a time series variable to predict the future

Answer 5

extrapolation models

Question 6

time series data set that contains no trend and the variance is constant.

Answer 6

stationary data

Question 7

time series data set that contains a trend.

Answer 7

non stationary data

Question 8

The degree of time lagged correlation in the time series

Answer 8

Autocorrelation

Question 9

Four Common Time Series Accuracy Measures?

Answer 9

1 Mean Absolute Deviation (MAD)
2 Mean square error (MSE) (most common)
3 Mean absolute percentage error (MAPE)
4 Root Mean Square Error (RMSE)

Question 10

A method for measuring the accuracy of a forecast by summing the absolute value of the differences between the actual and forecast values and dividing by the number of observations.
It is a standard error measurement that is used in time series analysis

Answer 10

Mean Absolute Deviation (MAD)

Question 11

A method for measuring the accuracy of a forecast by summing the squared differences dived by the number of observations
It penalizes the larger errors by squaring them

Answer 11

Mean square error (MSE) (most common)

Question 12

A method for measuring the accuracy of a forecast by summing the absolute percentage error
It is the average of absolute errors divided by actual observation values
It should not be used if there are zeros or near zeros in the actual data

Answer 12

Mean absolute percentage error (MAPE)

Question 13

What is the simple moving average known as?

Answer 13

The smoothing method

Question 14

A forecasting method based on averaging two or more consecutive time series data points

Answer 14

Simple moving average

Question 15

A forecasting method based on placing a larger weight on the more recent of two or more consecutive time series observations

Answer 15

Weighted moving average

Question 16

another averaging technique that is a forecasting method based on weighting the previous time series value using a smoothing coefficient

Answer 16

Exponential smoothing

Question 17

a technique that involves taking the average of the averages.

Answer 17

Double moving average

Question 18

• effective forecasting tool for time series data that exhibits a linear trend.
• it computes an estimate of the base, or expected, level of the time series, and the expected rate of increase or decrease per period

Answer 18

Double exponential smoothing (Holt’s Method)

Question 19

The process of separating a times series into its basic components. You can use this to communicate time series to management

Answer 19

Decomposition

Question 20

is the forecast value when the smooting coefficient for the simple exponential model is equal to one. (Also called the Naïve Model)
The forecast value when the smoothing value is one is the actual value for the previous period.

Answer 20

Previous actual value

Question 21

when your data does not have a trend or a seasonal component what forecast methods should you use?

Answer 21

moving average and single expo smoothing

Question 22

when your data has a seasonal component (with or without trend)

Answer 22

Decomposition or the Holt Winters method

Question 23

when your data has a trend but does not have a seasonal component

Answer 23

Trend analysis or double expo smoothing

Question 24

What are the four Time Series components?

Answer 24

Trend component
Seasonality component
Cyclic component
Irregular Component

Question 25

• the long-term direction of movement in a time series.
• Trends can be detected using a scatter diagram particularly after the data has been smoothed
• Can be either upward or downward

Answer 25

Trend component

Question 26

A graphic display of data plotted along two dimensions. In a time series analysis the X-axis is time
It is best used for detecting trends in a time series.

Answer 26

Scatter Diagram

Question 27

Because the moving average, weighted average, and exponential smoothing techniques use some average of the previous values to forecast the future values, they consistently underestimate _________ and overestimate _________.

Answer 27

1. underestimate the actual values if there is an upward trend in the data
2. overestimate the actual values if there is a downward trend

Question 28

Represents a pattern of change that is completed within one year and repeats itself regularly over the time series

Answer 28

Seasonality component

Question 29

Type of seasonality component that tends to be on the same order of magnitude each time a given season is encountered

Answer 29

Additive Effects

Question 30

Type of seasonality component that tends to have an increasing effect each time a given season is encountered

Answer 30

Multiplicative Effects

Question 31

The long-term, multi-year variations around the trend of a time series generally attributed to changing business and economic conditions

Answer 31

Cyclic component

Question 32

Components of Cyclical variation include:

Answer 32

1. Upswing
2. Contraction
3. Trough
4. Peak

Question 33

The short term, unanticipated and nonrecurring factors in a time series. They are short term fluctuations that are neither systematic nor predictable.

Answer 33

Irregular Component

Question 34

We can use build this type of model of a time series if data is available for one or more independent variables that account for the systematic movements in the time series.

Answer 34

Regression Model

Question 35

A process for fitting a straight line through a time series

Answer 35

Regression

Question 36

A method that uses regression analysis based on past time values to forecast future variable values

Answer 36

Autoregressive

Question 37

does not have a cause and effect relationship with the time series, but the behavior of a predictor variable might be correlated with that of the time series in a way that helps us forecast future values of time series

Answer 37

predictor variable

Question 38

a model that uses a straight diagonal trend line

Answer 38

Linear Trend Model

Question 39

this is the Y in the regression function. Sum of the functions x and the values ( a linear function of time)

Answer 39

systematic variation (predictable)

Question 40

the random variation in the time series not accounted for by our model (the error term of the equation)

Answer 40

unsystematic variation (unpredictable)

Question 41

using a curved trend line to the data

Answer 41

Quadratic Trend Model

Question 42

performance metrics recorded for numerous variables at the same point in time

Answer 42

Cross-sectional data

Question 43

How can we gain more accurate forecasts?

Answer 43

by combining the forecasts from several methods into a composite forecast

Question 44

If no one forecasting procedure is clearly better than the other it would be wise to?

Answer 44

combine the forecasts from the different procedures using a weighted average or some other method.