Chapter 5: Time-Based Data Flashcards
what are time series models
a time series model is obtained when you have values of a variable at successive intervals of time
what are some examples of the time series 4
- prices
- earnings
- production
- retail sales
what is the component of ‘trend’ in the time series
the longer term, underlying movement of the series
what does the term ‘cyclical’ represent in the time series
the wave-like movement caused by booms and slumps
what does the term ‘seasonal’ mean in the time series
the effects of the seasons- spring, summer, autumn, winter - on the series
what does the term ‘random/irregulity’ mean in the time series
the effects of factors such as strikes, breakdowns, etc on the series
what is the assumption of the additive model
it assumes that the values of the series are the result of adding the 3 components
how to calculate the observed value using the additive model
observed value = trend + seasonal + random
how to calculate the observed value using the multiplicative model
observed value = trend X season X random
how to work out the trend in the time series
the number of terms depends on the periodicity of the data
hoe do we work out the seasonal adjustments
we need to find the centered values
what is the standard method to finding the trend
use a moving average
is it important to find the value of these seasonal factors
yes, we need to find the values of the seasonal factors to obtain seasonally adjusted data and for forecasting purposes
when adjusting seasonally, do we add or subtract the seasonal factor
subtract, this is because we need to remove the seasonal factor in this scenario
when forecasting, do we need to include the seasonal factor?
yes, this is because during forecasting, we need to include the seasonal factor
what is forecasting
forecasting is the process of predicting future values based on past data
