Flashcards in Time Series Analysis Deck (16):

1

## Define time series.

###
A collection of well-defined data items obtained through repeated measurements over time.

Consistently measured at equally spaced time intervals.

Time is 1 variable (x-axis).

Has a natural order.

2

## Define time intervals.

### The time intervals at which the same data is collected.

3

## Examples of time intervals.

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Hour

Day

Week

Month

Quarter

Year

4

## What are the 3 components in a time series (the 3 component model)?

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Underlying trend (predictable)

Regular 'seasonal' component (predicable)

Irregular random fluctuations (unpredictable)

5

## Formula for the observed time series.

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OTS = UT + SC + RC

Observed time series = underling trend + seasonal component + random component

6

## Steps in time series analysis.

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Identify underlying trend

Predict seasonal effects

Remove seasonal component (seasonal adjustment)

Make a future prediction

7

## Define moving average.

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A technique used to obtain an overall idea of the trend.

Represents the 'middle' of a data set.

Uses a small window of data which is 'moved' through the whole time series.

8

## What should the size of a moving average be?

### Each 'season' should be represented once in the average.

9

## How to calculate a moving average.

###
Put a window over the required number of points.

Calculate the average.

Put the average in the middle of the window.

Slide window down one point.

10

## Define seasonal index.

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Used to predict the seasonal component.

Measure of the average effect of that season.

Average size of peaks compared to underlying trend.

11

## Define index.

### An indicator of the measure of something.

12

## How to make a prediction.

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Smooth data using moving average to uncover trend.

Do a linear regression on trend.

Make a future prediction.

Add or take the appropriate amount to account for the seasonal component.

13

## Purpose of seasonal adjustments.

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Allows for future predictions

Accounts for effect of future

Used for future predictions or missing data

14

## Formula for underlying trend/deseasonal adjustment.

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DA = OD ÷ SI

Deseasonal adjustment = observed data ÷ seasonal index

15

## Formula for seasonal adjustment.

###
SA = FP × SI

Seasonal adjustment = future prediction × seasonal index

16