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Chapter 17-21 > Chapter 17 > Flashcards

Chapter 17 Flashcards

1
Q

Define Time Series

A

a sequence of observations on a variable measured at successive points in time or over successive periods of time

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2
Q

Define Measurements

A

can be: Hourly, daily, weekly, monthly, yearly or at any other regular interval

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3
Q

Define Pattern of the data

A
  1. important factor in understanding how the time series has behaved in the past
  2. if such behavior can be expected in the future, we can use this past pattern to guide us in selecting an appropriate forecasting model
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4
Q

What is the first step in forecasting

A
  1. construct a time series plot
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5
Q

Describe a timeseries plot

A
  • graphical presentation of the relationship b/w time and the time series variable
  • time on horizontal axis and time series on the vertical
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6
Q

What are some common types of data patterns

A
  1. Horizontal pattern
  2. Trend Pattern
  3. Seasonal Pattern
  4. Trend and seasonal pattern
  5. Cyclical pattern
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7
Q

Describe Horizontal Pattern

A

data fluctuates around a constant mean

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8
Q

What is stationary series

A

used to denote a time series who statistical properties are INDEPENDENT of time

It means that

  1. the process generating the data has a constant mean
  2. the variability of the time series is constant over time
    - always have a horizontal pattern
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9
Q

Is simply observing a horizontal patter enough to conclude that the time series is stationary?

A

no

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10
Q

What is a trend pattern

A
  • a time series pattern may also show gradual shifts of movement to relatively higher or low values over a longer period of time
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11
Q

What can cause trend pattern

A
  • usually due to population increases or decreases
  • changes in demographic characteristics of the population
  • technology, and / or consumer preferences
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12
Q

What are exponential relationships

A

are appropriate when the % change from one period to the next is relatively constant

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13
Q

What is seasonal patterns

A
  • seeing the same repeating patterns over successive periods of time
  • ex. Pool co. expects lower sales in fall and winter months Peak sales in spring and summer
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14
Q

Do seasonal influences indicate any long term trend?

A

no

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15
Q

what is Trend and seasonal pattern

A
  • combination of a trend and seasonal pattern

- need a forecasting method that has the capabilities of dealign with both trend and seasonality

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16
Q

what is cyclical pattern

A

an alternating sequence of points below and above the trend line lasting more than one year
- 0ften the cyclical component is due to multi-year business cycles

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17
Q

What is important to note about cyclical patterns

A

business cycles are extremely difficult if not impossible to forecast
- as a result, cyclical effects are often combined with long-term trend effects and called trend-cycle effects

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18
Q

What are the Methods for forecasting

A
  1. Time series 2. Casual method
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19
Q

What can forecasting methods be?

A
  1. Qualitative - Judgement

2. Quantitative

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20
Q

What is needed for quantitative forecasting

A
  1. past info about the variable being forecasted is available
  2. the info can be quantified
  3. it is reasonable to assume the pattern of the past will continue
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21
Q

What is time series forecasting

A
  1. historical data restricted to past values

2. based solely on past values and or past forecast errors

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22
Q

What is the objective to time series

A

discover a pattern in historical data or time series

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23
Q

What is the objective to time series

A

discover a pattern in historical data or time series

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24
Q

What kinds of quantitative forecasting methods are there

A
  1. Naive
  2. Moving average
  3. weight moving average
  4. exponential smoothing
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25
Q

What is the simplest forecasting method

A

Naive

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26
Q

What is the forecast error

A

Forecast error = actual value - forecast

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27
Q

How do you perform Naïve method

A

use the previous period to forecast the next

28
Q

If the forecast error is positive what can we say

A

the forecasting method overestimated the actual value forecasted

29
Q

if the forecasting error is negative what can we say

A

the forecasting model underestimated the actual value forecasted

30
Q

What are the Measures of Accuracy

A
  1. MAE
  2. MSE
  3. MAPE
31
Q

What is the formula for MAE

A

avg. of absolute value of forecast errors (absolute (positive only) forecast errors / n

32
Q

What is the formula for MSE

A

MSE = Avg of the sum of the squared forecast errors

forecast errors / n-1

33
Q

What is the formula for MAPE

A

MAPE = Forecast error / actual x 100

34
Q

What does MAE stand for

A

Absolute Mean Error

35
Q

What does MSE stand for

A

Mean Squared Error

36
Q

What does MAPE stand for

A

The Mean Absolute Percent Error

37
Q

What is MAE useful for

A

this measure avoids the problems of positive and negative forecast errors offsetting one another

38
Q

What is MSE useful for

A

this measure also avoids the problem of negative and positive forecast errors offsetting each other

39
Q

FOr every measure, what provides more accurate forecast than using the most recent observations as the forecast for the next period

A

the average of historical values

40
Q

if the underlying time series is stationary, the average of all historical data will

A

always provide the best results

41
Q

What if the underlying time series is NOT stationary? Why could that be?

A

could be due to changes in business conditions (ie contract for certain amount) can often result in a time series that has a horizontal pattern shifting to a new level

  • this would take a long time for the forecasting that uses the avg of all historical data to adjust
  • in this case, the simple naive method adjusts very rapidly b/c it uses the most recent data
42
Q

Which method adjusts rapidly

A

Naive method because it uses the most recent data

43
Q

Describe the Weighted Moving average Method

A

each observation in the average calculation receives the same weight

44
Q

Describe the variation called weighted moving average

A

select a different weight for each data value and then compute a weighted average of the most recent k values as the forecast

  • most cases the most recent observations received the most weight, then the weight decreases for remaining older data values
  • the sum of the weights = 1

ie. 3/6 - most recent
2/6 - second most recent
1/6 - the 3rd most recent

45
Q

if we think that the recent past is a better predictor of the future than the distant past what should we do with the weighted average

A

larger weights s/b given to the more recent observations

46
Q

What if the time series is highly variable ( in weight average)

A
  • selecting approx equal weights is best

- only requirement is that the weights add up to 1

47
Q

What can we use to determine whether one particular combination of data values and weights provides a more accurate forecast form another

A

use MSE as the measure of accuracy

- use the combination of # of data values and weights that minimizes MSE

48
Q

What is exponential smoothing

A
  • also uses weighted average
  • se select only one weight - the weight for the most recent observation
  • weights for the rest are computed automatically
  • become smaller weights the further away
49
Q

What is the formula for exponential smoothing

A

Ft+1 = aYt + (1-a)Ft

50
Q

what is Ft+1

A

forecast of the time series for period t +1

51
Q

what is Yt

A

actual value of the time series in period t

52
Q

What is Ft

A

forecast of the time series for period t

53
Q

what is a

A

alpha = smoothing constant (form 0 to 1)

54
Q

Which methods adapt well to to changes in the level of a horizontal pattern

A
  1. moving averages
  2. weighted moving averages
  3. Exponential smoothing
55
Q

When are moving average, weighted average and exponential not appropirate for

A

without modifications, not appropriate when significant tend, cyclical or seasonal effects are present

56
Q

What is the objective to moving average, weight average and exponential smoothing

A

to “smooth out” the random fluctuations

  • called smoothing methods
  • easy to use
  • provide a high level of accuracy for short range forecasts (ie next period)
57
Q

When do you use Exponential smoothing method

A

used when we have no particular pattern, no seasonal variation, no weekly variation, just a series of numbers

  • make a forecast based on the previous result and then correct it by how much the previous one was out
58
Q

What are the exponential smoothing models

A

add

59
Q

If the time series contains substantial random variability what can you use and why

A

a small value of the smoothing constant a is preferred

  • b/c if much of the forecast error is due to random variability, we do not want to overreact and adjust too quickly
60
Q

What do Larger values of a provide

A

the advantage of quickly adjusting the forecast

- react more quickly to changing conditions

61
Q

How do you determine a desirable value for a

A
  • choose a value for a that minimizes MSE
62
Q

What is the linear trend equation

A

Tt = bo + b1t

63
Q

What does Holt’s linear exponential smoothing do

A

forecast a time series with a linear trend

  • uses two smoothing constants, a and B
  • has 3 equations
64
Q

What is non-linear trend regression used for

A

a series that have a curvilinear or nonlinear trend

65
Q

What is the formula for non-linear trend regression

A

Tt = b0+b1t + b2t squared

66
Q

What is the exponential trend equation used for

A

another alternative that can be used to model the non-linear pattern

67
Q

What is the formula for exponential trend

A

Tt = b0(b1)exponent t

Chapter 17-21 (7 decks)

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