Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Business Systems > Time Series Data > Flashcards

Time Series Data Flashcards

1
Q

Time Series

A

Sequence of data points, typically measured at successive points in time.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

True or false: Observations that are close together in time are generally more closely related than observations further apart.

A

True

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Univariate time-series analysis

A

Using a single sequence of data for a given sequence of time periods

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Multivariate time-series analysis

A

Using multiple sets of data captured for the same sequence of time periods

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

The components of a time series

A
  • Period - Trend - Seasonal Variation - Cyclical Variation - Stationary - Noise
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Period

A

The unit of the analysis which is equal to discrete time intervals when the data measurements are taken

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Trend

A

The long-term movement of the time series. It could be increasing, decreasing, or stationary.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Seasonal Variation

A

When a time series depicts a repetitive pattern that is observed over some time horizon lag. These effects are typically associated with calendar or climatic changes.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Cyclical Variation

A

An upturn or downturn that is not tied to seasonal variation. These effects usually result from changes in economic conditions.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Stationary

A

When the data fluctuates about a mean.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Non-stationary

A

When the data does not fluctuate about a mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Noise

A

Any non-repeating, non-specific pattern which exists for a short duration

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What are the components that can be separated from a time series via decomposition?

A

Trend (T) Seasonal (S) Random (R) Sometimes Cycle (C)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What is the benefit of decomposing time series?

A

Decomposition allows for seasonal adjustment and more clear identification of trends.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Fill in the blank: The decomposed components of a time series are assumed to follow either a(n) ____ or a(n) ____ model.

A

Additive Multiplicative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

When are additive model useful?

A

Additive models are useful when seasonal variation is relatively constant over time.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

When are multiplicative models useful?

A

Multiplicative models are useful when seasonal variation increases over time.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Additive Model

A

yt = T + S + R

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Multiplicative Model

A

yt = T * S * R

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What do T, S, R, and C stand for in time series models?

A

T - Trend S - Seasonal R - Random C - Cycle

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Forecasting

A

Process of estimating outcomes of events in a future state.

22
Q

Fill in the blank: It is important to understand ____ and ____ while forecasting outcomes and one must indicate the degree of ____ attached to the forecasts.

A

Risk Uncertainty Uncertainty

23
Q

Prediction Interval

A

An estimate of an interval in which a future observation will fall with a certain probability.

24
Q

Which is narrower: The range of a 95% prediction interval or a 99% prediction interval.

A

95% prediction interval

25
Q

What are the two types of forecasting approaches?

A

Qualitative Quantitative

26
Q

Qualitative Forecasting Approaches

A

Subjective and based upon opinions and judgement of consumers or experts.

27
Q

Quantitative Forecasting Approaches

A

Based upon analysis of data using mathematical techniques.

28
Q

When are qualitative forecasting approaches more appropriate?

A

When past data are not available

29
Q

When are quantitative forecasting approaches more appropriate?

A

When past data are available

30
Q

Time Series Analysis for Forecasting

A

Using historical data as the basis of estimating future outcomes

31
Q

What are five methods of time series analysis for forecasting?

A

Moving Average Weighted Moving Average Exponential Smoothing Autoregressive Moving Average (ARMA) Autoregressive Integrated Moving Average (ARIMA)

32
Q

What are some questions you should ask when working with data?

A

What is the source of the data? How was the data recorded? When was the data recorded? Has the data been processed? Are there any missing data points? What units were used to measure the data?

33
Q

Naive Model

A

The forecast value for the next observation is simply what was observed this time. Ft+1 = Yt

34
Q

What are the four methods of measuring forecast errors?

A

BIAS - Arithmetic Mean of Error MAD - Mean Absolute Deviation MSE - Mean Squared Error MAPE - Mean Absolute Percentage Error

35
Q

BIAS

A

Arithmetic Mean of Error ( Σ ( Error ) ) / n

36
Q

MAD

A

Mean Absolute Deviation ( Σ | Error | ) / n

37
Q

MSE

A

Mean Squared Error ( Σ ( Error^2) ) / n

38
Q

MAPE

A

Mean Absolute Percentage Error 100 * ( Σ ( | Error | / Actual Value ) ) / n

39
Q

Error

A

Actual Value - Forecast Value

40
Q

Absolute Error

A

Error |

41
Q

Squared Error

A

Error^2

42
Q

Smoothing Time Series

A

Local averaging of data in order to smooth out any short-term fluctuations and highlight longer-term trends or cycles.

43
Q

What is the most common technique of smoothing time series?

A

Moving Average

44
Q

Moving Average

A

Replaces the underlying data series by either a simple or weighted average of a specified number of elements.

45
Q

Simple Moving Average

A

An equally weighted mean of the previous k consecutive data points.

46
Q

When would you use a smaller value of k for SMA?

A

When there are sudden shifts in the underlying data

47
Q

When would you use a larger value of k for SMA?

A

When fluctuations are infrequent in the underlying data.

48
Q

When should you use moving average?

A

Non-seasonal time series Short term prediction

49
Q

Weighted Moving Average

A

A weighted mean of the previous k consecutive data points, in which recent observations are given more weight. Note: The sum of the weights should be 1

50
Q

The first step in time series analysis is what?

A

Plot the data

Business Systems (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions