Time series models

Question 1

describe time series models

Answer 1

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

Question 2

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

Answer 2

—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

Question 3

list components of a time series

Answer 3

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

Question 4

describe features of long term trend

Answer 4

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

Question 5

describe features of cyclical variation

Answer 5

—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 **

Question 6

describe features of seasonal variation

Answer 6

—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”

Question 7

describe features of random variation

Answer 7

• —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