Chapter 12 Business Analytics
Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for:
Seasonality
Holt's model differs from simple exponential smoothing in that it includes a term for
Trend
A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span
True
A time series can consist of four different components: trend, seasonal, cyclical, and random (or noise)
True
A time series is any variable that is measured over time in sequential order.
True
A trend component of a time series is a long-term, relatively smooth pattern or direction exhibited by a series, and its duration is more than one year
True
An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values
True
An exponential trend is appropriate when the time series changes by a constant percentage each period
True
Correlogram is a bar chart of autocorrelation at different lags
True
Econometric forecasting models, also called causal models, use regression to forecast a time series variable by using other explanatory time series variables
True
Every form of exponential smoothing model has at least one smoothing constant, which is always between 0 and 1.
True
Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else, except possible time itself - to forecast values of the variable
True
If a time series exhibits an exponential trend, then a plot of its logarithm should be approximately linear
True
In a multiplicative seasonal model, we multiply a "base" forecast by an appropriate seasonal index. These indexes, one for each season, typically average to 1
True
In an additive seasonal model, we add an appropriate seasonal index to a "base" forecast. These indexes, one for each season, typically average to 0
True
Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality
True
The most common form of autocorrelation is positive autocorrelation, where large observations tend to follow large observations and small observations tend to follow small observations
True
The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods
True
The purpose of using the moving average is to take away the short-term seasonal and random variation, leaving behind a combined trend and cyclical movement.
True
The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages
True
The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels, trends and cycles in the data
True
The time series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called cyclical.
True
You will always get more accurate forecasts by using more complex forecasting methods.
True
Econometric models can also be called:
a casual models
When using the moving average method, you must select which represent(s) the number of terms in the moving average
a span
Examples of non-random patterns that may be evident on a time series graph include
all of these options
The components of a time series include:
all of these options
In a random series, successive observations are probabilistically independent of one another. If this property is violated, the observations are said to be:
autocorrelated
A linear trend means that the time series variable changes by a:
constant amount each time period
In contrast to linear trend, exponential trend is appropriate when the time series changes by a:
constant percentage each time period
The most common form of autocorrelation is positive autocorrelation, in which
large observations tend to follow large observations and small observations tend to follow small observations
Perhaps the simplest and one of the most frequently used extrapolation methods is the
moving average
Models such as moving average, exponential smoothing, and linear trend use only
previous values Y of to forecast future values of Y
When using exponential smoothing, a smoothing constant must be used. The value for
ranges between 0 and 1
When using Holt's model, choosing values of the smoothing constant that are near 1 will result in forecast models which
react very quickly to changes in the trend
The moving average method can also be referred to as a (n) method
smoothing
The forecast error is the difference between
the actual value and the forecast
ranges between 0 and 1
values of near 1
A regression approach can also be used to deal with seasonality by using variables for the seasons.
Dummy
Extrapolation methods attempt to
Extrapolation methods attempt to
A meandering pattern is an example of a random time series
False
Holt's method is an exponential smoothing method, which is appropriate for a series with seasonality and possibly a trend
False
If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component
False
If the span of a moving average is large - say, 12 months - then few observations go into each average, and extreme values have relatively large effect on the forecasts
False
If we use a value close to 1 for the level smoothing constant and a value close to 0 for the trend smoothing constant in Holt's exponential smoothing model, then we expect the model to respond very quickly to changes in the level, but very slowly to changes in the trend
False
The seasonal component of a time series is harder to predict than the cyclic component; the reason is that cyclic variation is much more regular
False
The time series component that reflects a long-term, relatively smooth pattern or direction exhibited by a time series over a long time period, is called seasonal
False
The trend line was calculated from quarterly data for 2000 - 2004, where = 1 for the first quarter of 2000. The trend value for the second quarter of the year 2005
False
To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.
False
We compute the five-period moving averages fir all time periods except the first two
False
Winter's method is an exponential smoothing method, which is appropriate for a series with trend but no seasonality.
False
Suppose that a simple exponential smoothing model is used (with = 0.40) to forecast monthly sandwich sales at a local sandwich shop. The forecasted demand for September was 1560 and the actual demand was 1480 sandwiches. Given this information, what would be the forecast number of sandwiches for October?
1528
Suppose that a simple exponential smoothing model is used (with a = 0.30) to forecast monthly sandwich sales at a local sandwich shop. After June's demand is observed at 1520sandwiches, the forecasted demand for July is 1600 sandwiches. At the beginning of July, what would be the forecasted demand for August?
1600
The linear trend was estimated using a time series with 20 time periods. The forecasted value for time period 21 is
162
