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Time series (Predicting the future:Some time series exhibit predictability…
Time series
Predicting the future:Some time series exhibit predictability when strong periodic or seasonal patterns are present. Other time series exhibit predictability when positive autocorrelation - or correlation among neighboring observations - induces what appear as short-term trends.
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Sample Transformations: diff(..,s) seasonal difference transformation, can remove periodic trends
White Noise Model:
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A fixed, constant variance
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Forecast
ts(mydata[,2:4], start = c(1981, 1), frequency = 4): Creating time series objects
Time series plots: autoplot(), gglagplot(), ggAcf()
forecast and ggplot2, fpp2
Seasonal plots: ggseasonplot(polar = T), ggsubseriesplot()
Trends, seasonality and cyclicity
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Forecast
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Well done! A good model forecasts well (so has low RMSE on the test set) and uses all available information in the training data (so has white noise residuals).
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Autocorrelation:(lagged correlation) are used to assess whether a time series is dependent on its past
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acf(..., lag.max = 1, plot = FALSE)
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