Time series models Flashcards

1
Q

describe time series models

A

—Data recorded over time
Can be used for forecasting future events, or planning for how to deal with the future

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2
Q

time series models can be used to forecast. Forecasting can be..

A

—Forecasting can be
—Qualitative – based on expert opinion
—Quantitative – based on statistical models – this requires historical data, and a willingness to assume the historical pattern will repeat

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3
Q

list components of a time series

A
  1. Long term trend
  2. Cyclical variation
  3. Seasonal variation
  4. Random variation
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4
Q

describe features of long term trend

A

—Also called secular trend
—Relatively smooth pattern or direction
—Can be linear or non-linear

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5
Q

describe features of cyclical variation

A

—Wave-like pattern describing long term trend apparent over a number of years – cyclical effect
—Recurrence period over 1 year (definition)
—E.g. business cycles with periods of economic recession and inflation, long-term product demand cycles, monetary and financial sector cycles
—Rare to find cyclical patterns that are **consistent and predictable **

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6
Q

describe features of seasonal variation

A

—Cycles that occur over short repetitive calendar periods
—Duration less than one year (definition)
—“Seasonal” may mean 4 seasons, or systematic patterns over a month/week/day
—E.g. restaurant demand features “seasonal” variation throughout the day
—Also, consider ABS data “seasonally adjusted”

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7
Q

describe features of random variation

A
  • —Irregular, unpredictable changes
  • —Not caused by other components (trend, cyclical, seasonal variation)
  • —Often referred to as “noise”
  • —Can mask the existence of other components
  • —Exists in all time series
  • —Goal of most time series analysis is to reduce impact of random variation on forecasting or interpretation
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