QMB 3250 EXAM 3 Modules(17-24)-QUESTIONS
WITH 100% CORRECT SOLUTIONS!!
If there is no direction in the time series trend, this is called...
Stationary to the mean.
Seasonal Component (time series)
Fluctuation occurs at roughly the same time each year (or day of the week or part of the month).
The time between two peaks is called...
Period
Components of a Time Series
1. Trend
2. Seasonal
3. Long Term Cycles
4. Irregular Components
Types of smoothing methods
1. Simple Moving Average
2. Single Exponential Smoothing Average (SES)
Simple Moving Average
- this method creates a new time series from the original data by averaging adjacent values.
- the number of values that are averaged is called the length (L).
- the purpose is that it helps illuminate how the data is varying over time.
,Disadvantages of simple moving average
-The moving average values can be affected by outliers (sharp peaks or falls).
- Doesn't work well when there are strong seasonal, trend or cyclical components.
Weighted Moving Averages
A more sophisticated type of moving averages gives different weights to the values being
averaged. Values that are further away get less weight.
Types of weighted moving averages
1. simple exponential smoothing
2. auto regressive moving averages
Simple Exponential Smoothing
values that are closest to the estimated value get the most weight in the moving average and
values that are further away get less.
* doesn't usually work well when seasonal components are present.s
Alpha for Weighted Exponential Smoothing
- Alpha is close to 0.5, the most recent value and historic values are weighed the same .
- Alpha is close to 1, the most recent value is given more weight. this is used if you want the
series to react more quickly to irregular components.
- Alpha is close to 0, the most recent value is given less weight than historic valleys. you want to
use this if you want the series to react more slowly, creating a more stable series.
Data collected overtime at regular intervals, we have a...
,time series (daily, weekly, monthly, quarterly, yearly).
If there is no direction on the trend (data), it is called...
stationary to the mean.
Seasonal components
Fluctuation occurs that roughly the same time each year (or day of the week or part of the
month).
Fluctuation for seasonal components is of the same magnitude and in the same direction?
true
Long-term cycles
you may also see the business cycle if you have long enough period of data to see this trend
Irregular component
- sometimes there's variation that is not explained.
- just like residuals, you should look at these values for anything unusual.
There are two different types of modeling Time series...
1. smoothing methods
2. regression methods
Smoothing methods
- smooth out a regularities..
- there are no assumptions that must be made about the trend or seasonal component.
, - a disadvantage is that predictions are limited - they can only predict in the very short term.
- these are usually only used for one period beyond the data recorded.
Regression methods
- model the behavior of the trend and cycle using methods that we learned in regression.
- the advantage is that they can be used to predict further into time, although we should still be
cautious about extrapolating too far from the data.
Longer periods in simple moving average ofwill have moving averages that respond more
slowly, so longer periods are...
smoother
MSE - Mean Squared Error
- sums the squares of the errors
- disadvantages (large deviations, not the same units in the data, it's value is changed if the scale
of y is changed.
MAD - Mean Absolute Deviations
- takes me absolute value of the errors.
- advantages (doesn't have as large of a penalty for one irregular event, it is in the same units of
the data)
- disadvantage ( it's value is changed if the scale of y is changed)
* this is called MAE in JMP.
MAPE - Mean Absolute Percentage Error
WITH 100% CORRECT SOLUTIONS!!
If there is no direction in the time series trend, this is called...
Stationary to the mean.
Seasonal Component (time series)
Fluctuation occurs at roughly the same time each year (or day of the week or part of the month).
The time between two peaks is called...
Period
Components of a Time Series
1. Trend
2. Seasonal
3. Long Term Cycles
4. Irregular Components
Types of smoothing methods
1. Simple Moving Average
2. Single Exponential Smoothing Average (SES)
Simple Moving Average
- this method creates a new time series from the original data by averaging adjacent values.
- the number of values that are averaged is called the length (L).
- the purpose is that it helps illuminate how the data is varying over time.
,Disadvantages of simple moving average
-The moving average values can be affected by outliers (sharp peaks or falls).
- Doesn't work well when there are strong seasonal, trend or cyclical components.
Weighted Moving Averages
A more sophisticated type of moving averages gives different weights to the values being
averaged. Values that are further away get less weight.
Types of weighted moving averages
1. simple exponential smoothing
2. auto regressive moving averages
Simple Exponential Smoothing
values that are closest to the estimated value get the most weight in the moving average and
values that are further away get less.
* doesn't usually work well when seasonal components are present.s
Alpha for Weighted Exponential Smoothing
- Alpha is close to 0.5, the most recent value and historic values are weighed the same .
- Alpha is close to 1, the most recent value is given more weight. this is used if you want the
series to react more quickly to irregular components.
- Alpha is close to 0, the most recent value is given less weight than historic valleys. you want to
use this if you want the series to react more slowly, creating a more stable series.
Data collected overtime at regular intervals, we have a...
,time series (daily, weekly, monthly, quarterly, yearly).
If there is no direction on the trend (data), it is called...
stationary to the mean.
Seasonal components
Fluctuation occurs that roughly the same time each year (or day of the week or part of the
month).
Fluctuation for seasonal components is of the same magnitude and in the same direction?
true
Long-term cycles
you may also see the business cycle if you have long enough period of data to see this trend
Irregular component
- sometimes there's variation that is not explained.
- just like residuals, you should look at these values for anything unusual.
There are two different types of modeling Time series...
1. smoothing methods
2. regression methods
Smoothing methods
- smooth out a regularities..
- there are no assumptions that must be made about the trend or seasonal component.
, - a disadvantage is that predictions are limited - they can only predict in the very short term.
- these are usually only used for one period beyond the data recorded.
Regression methods
- model the behavior of the trend and cycle using methods that we learned in regression.
- the advantage is that they can be used to predict further into time, although we should still be
cautious about extrapolating too far from the data.
Longer periods in simple moving average ofwill have moving averages that respond more
slowly, so longer periods are...
smoother
MSE - Mean Squared Error
- sums the squares of the errors
- disadvantages (large deviations, not the same units in the data, it's value is changed if the scale
of y is changed.
MAD - Mean Absolute Deviations
- takes me absolute value of the errors.
- advantages (doesn't have as large of a penalty for one irregular event, it is in the same units of
the data)
- disadvantage ( it's value is changed if the scale of y is changed)
* this is called MAE in JMP.
MAPE - Mean Absolute Percentage Error
