library(fpp3) # White noise my_data <- tsibble(t = seq(100), y = rnorm(100), index = t) my_data |> autoplot(y) my_data |> ACF(y) |> autoplot() my_data |> gg_tsdisplay(y, plot_type = "histogram") tsibble(t = seq(100), y = rnorm(100), index = t) |> gg_tsdisplay(y, plot_type = "histogram") # Random walks tsibble(t = seq(100), y = cumsum(rnorm(100)), index = t) |> gg_tsdisplay(y, plot_type = "histogram") # Australian electricity production aus_production aus_production |> autoplot(Electricity) aus_production |> autoplot(box_cox(Electricity, lambda = .52)) lambda <- aus_production |> features(Electricity, guerrero) aus_production |> autoplot(box_cox(Electricity, lambda = lambda)) dcmp <- aus_production |> model(stl = STL(box_cox(Electricity, lambda = 0.5))) dcmp |> components() |> autoplot() components(dcmp) |> gg_subseries(season_year) aus_production |> autoplot(Electricity, color = "gray") + autolayer(components(dcmp), inv_box_cox(trend, lambda = 0.5), color = "red" ) aus_production |> autoplot(Electricity, color = "gray") + autolayer(components(dcmp), inv_box_cox(season_adjust, lambda = 0.5), color = "red") aus_production |> model(STL(box_cox(Electricity, 0.5))) |> components() |> autoplot() # OTexts STl decomposition (from 2022 exam) otexts_views |> autoplot(Pageviews) otexts_views |> model(STL(Pageviews ~ trend(window = 31))) |> components() |> autoplot() # ABS employment problem employed <- tsibble( Time = yearmonth("1978 Feb") + 0:439, Employed = c( 5985.7, 6040.6, 6054.2, 6038.3, 6031.3, 6036.1, 6005.4, 6024.3, 6045.9, 6033.8, 6125.4, 5971.3, 6050.7, 6096.2, 6087.7, 6075.6, 6095.7, 6103.9, 6078.5, 6157.8, 6164.0, 6188.8, 6257.2, 6112.9, 6207.2, 6278.7, 6224.9, 6273.4, 6269.9, 6314.1, 6281.4, 6360.0, 6320.2, 6342.0, 6426.6, 6253.0, 6356.5, 6428.1, 6426.3, 6412.4, 6413.9, 6425.3, 6393.7, 6502.7, 6445.3, 6433.3, 6506.9, 6355.5, 6432.4, 6497.4, 6431.6, 6440.9, 6414.3, 6425.9, 6379.3, 6443.5, 6421.1, 6366.8, 6370.1, 6172.0, 6263.9, 6310.3, 6254.5, 6272.8, 6266.5, 6295.0, 6241.2, 6358.2, 6336.1, 6377.5, 6456.5, 6251.4, 6365.4, 6503.2, 6477.6, 6489.7, 6499.0, 6528.7, 6466.1, 6579.8, 6553.2, 6576.1, 6636.0, 6452.4, 6595.7, 6657.4, 6588.8, 6657.9, 6659.4, 6703.4, 6675.5, 6814.7, 6771.1, 6881.9, 6910.8, 6753.6, 6861.9, 6961.9, 6997.9, 6979.0, 7007.7, 6991.5, 6918.5, 7040.6, 7030.4, 7034.2, 7116.8, 6902.5, 7022.3, 7133.4, 7109.6, 7103.5, 7128.9, 7175.6, 7092.3, 7186.5, 7177.4, 7182.2, 7330.7, 7169.4, 7247.3, 7397.4, 7383.4, 7354.8, 7378.3, 7383.1, 7353.3, 7503.2, 7477.3, 7508.6, 7622.9, 7423.8, 7566.5, 7634.6, 7678.4, 7720.8, 7711.0, 7740.8, 7715.3, 7841.6, 7806.5, 7862.4, 7935.5, 7707.7, 7803.0, 7874.1, 7887.9, 7908.5, 7900.3, 7919.4, 7808.0, 7905.5, 7848.9, 7826.9, 7915.5, 7641.3, 7708.7, 7715.4, 7717.2, 7703.7, 7678.1, 7583.0, 7620.7, 7713.2, 7638.0, 7614.9, 7712.2, 7518.9, 7597.2, 7646.2, 7644.1, 7631.4, 7637.3, 7668.3, 7613.4, 7709.7, 7665.7, 7587.4, 7693.4, 7533.7, 7531.0, 7645.7, 7572.6, 7620.5, 7627.9, 7646.5, 7589.4, 7747.6, 7738.8, 7744.9, 7842.1, 7646.8, 7738.6, 7824.2, 7827.4, 7857.9, 7878.4, 7966.0, 7861.7, 8054.4, 7997.2, 8003.3, 8135.5, 7928.4, 8049.9, 8118.1, 8174.6, 8165.2, 8205.6, 8229.0, 8165.9, 8300.4, 8232.6, 8300.3, 8395.7, 8166.7, 8246.6, 8280.4, 8248.0, 8297.1, 8311.7, 8332.1, 8265.9, 8373.0, 8319.4, 8314.4, 8431.4, 8235.2, 8291.4, 8347.5, 8343.1, 8330.2, 8345.6, 8374.9, 8250.3, 8474.0, 8405.2, 8462.1, 8540.5, 8334.7, 8413.0, 8460.0, 8499.9, 8482.5, 8516.8, 8541.9, 8455.2, 8653.2, 8601.0, 8554.3, 8696.5, 8477.4, 8556.5, 8618.9, 8631.9, 8606.5, 8673.2, 8706.7, 8603.6, 8777.1, 8755.3, 8763.7, 8900.7, 8628.2, 8754.4, 8830.7, 8882.2, 8865.0, 8922.0, 9020.0, 8911.6, 9061.3, 8973.1, 8912.7, 9059.6, 8834.9, 8920.9, 8956.0, 9023.6, 9004.6, 9021.9, 9048.9, 8971.9, 9105.9, 9058.7, 9055.6, 9177.1, 8993.4, 9092.3, 9128.5, 9129.5, 9134.7, 9180.8, 9194.5, 9150.3, 9303.5, 9249.1, 9286.7, 9439.7, 9281.7, 9372.6, 9362.1, 9365.6, 9380.1, 9370.4, 9363.9, 9327.0, 9486.1, 9447.8, 9427.7, 9573.6, 9363.8, 9434.5, 9506.4, 9512.0, 9533.5, 9543.3, 9553.1, 9462.1, 9668.6, 9662.2, 9652.9, 9807.8, 9634.4, 9744.6, 9828.3, 9856.3, 9850.8, 9896.6, 9912.3, 9870.3, 10004.6, 9949.7, 9945.0, 10074.7, 9842.7, 9961.1, 10048.7, 10041.0, 10082.1, 10120.8, 10170.8, 10105.8, 10299.5, 10212.4, 10201.6, 10404.3, 10156.1, 10277.0, 10349.2, 10362.9, 10412.0, 10436.3, 10456.8, 10406.4, 10588.8, 10520.5, 10535.0, 10710.1, 10524.9, 10622.9, 10677.4, 10706.2, 10690.3, 10745.0, 10761.9, 10710.4, 10854.5, 10807.4, 10757.3, 10915.6, 10681.0, 10776.7, 10775.2, 10792.7, 10786.8, 10770.9, 10808.8, 10707.3, 10882.1, 10845.2, 10829.2, 11010.9, 10809.9, 10889.2, 10928.9, 10940.1, 10957.4, 11009.3, 11030.5, 10973.8, 11159.4, 11129.0, 11144.5, 11295.0, 11063.7, 11146.2, 11217.0, 11186.5, 11196.2, 11221.3, 11227.5, 11130.7, 11321.2, 11274.0, 11240.6, 11354.8, 11159.0, 11236.2, 11332.4, 11328.3, 11389.0, 11350.6, 11363.7, 11259.8, 11452.6, 11401.9, 11375.0, 11518.4, 11304.0, 11424.3, 11436.3, 11482.2, 11495.6, 11497.8, 11486, 11369, 11547, 11499, 11472, 11571, 11354, 11493, 11562, 11589, 11595, 11602, 11590, 11622, 11593 ), index = Time ) |> mutate( Month = month(Time, label = TRUE), Year = year(Time) ) |> select(Time, Month, Year, Employed) employed |> filter(Year >= 2005) |> autoplot(Employed) + labs(title = "Total employed", y = "Thousands") employed |> filter(Year >= 2005) |> gg_season(Employed, labels = "right") + labs(title = "Total employed", y = "Thousands") employed |> mutate(diff = difference(Employed)) |> filter(Month == "Sep") |> ggplot(aes(y = diff, x = 1)) + geom_boxplot() + coord_flip() + labs(title = "Sep - Aug: total employed", y = "Thousands") + scale_x_continuous(breaks = NULL, labels = NULL) dcmp <- employed |> filter(Year >= 2005) |> model(stl = STL(Employed ~ season(window = 11), robust = TRUE)) components(dcmp) |> autoplot() components(dcmp) |> filter(year(Time) == 2013) |> gg_season(season_year) + labs(title = "Seasonal component") + guides(colour = "none") components(dcmp) |> as_tsibble() |> autoplot(season_adjust)