Chapter 12 Time Series Analysis and Forecasting

Extrapolation methods

Forecasting methods where only past values of a variable (and possible time itself) are used to forecast future values

Seasonality

A regular pattern of ups and downs based on the season of the year, typically months or quarters

Smoothing constants

Constants between 0 and 1 that prescribe the weight attached to previous observations and hence the smoothness of the series of forecasting

Correlogram

A bar chart of autocorrelations at different lags

Exponential smoothing models

A class of forecasting models where forecasts are based on weighted averages of previous observations, giving more weight to more recent observations

Moving averages model

A forecasting model where the average of several past observations is used to forecast the next observation

Ratio-to-moving averages method

A method for deseasonalizing a time series, so that some other method can then be used to forecast the deseasonalized series

Random walk model

A model indicating that the differences between adjacent observations of a time series variable are constant except for random noise

Linear trend model

A regression model where a time series variable changes by a constant amount each time

Exponential trend model

A regression model where a time series variable changes by a constant percentage each time period

Autoregression model

A regression model where the only explanatory variables are lagged values of the dependent variable (and possibly other time series variables or their lags)

Dummy variables for seasonality

A regression-based method for forecasting seasonality, where dummy variables are used for the season

Trend

A systematic increase or decrease of a time series variable through time

Runs test

A test of whether the forecast errors are random noise

Simple exponential smoothing

An exponential smoothing model useful for time series with no prominent trend or seasonality

Cyclic variation

An irregular pattern of ups and downs caused by business cycles

Autocorrelations

Correlations of a time series variable with lagged versions of itself

Causal (or econometric) methods

Forecasting methods based on regression, where other time series variables are used as explanatory variables

Mean absolute error (MAE)

The average of the absolute forecast errors

Mean absolute percentage error (MAPE)

The average of the absolute percentage forecast errors

Forecast error

The difference between the actual value and the forecast

Span

The number of observations in each average of a moving averages model

Root mean square error (RMSE)

The square root of the average of the squared forecast errors

Noise (or random variation)

The unpredictable ups and downs of a time series variable