1 Yearly PIs data

1.1 Load data:

yearly_ts <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_yearly_TSTS.csv", stringsAsFactors = FALSE)
yearly_fs <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_yearly_PIs_FTS.csv", stringsAsFactors = FALSE)
head(yearly_ts)
##   series_id category   value timestamp
## 1        Y1    MICRO  940.66      1975
## 2        Y1    MICRO 1084.86      1976
## 3        Y1    MICRO 1244.98      1977
## 4        Y1    MICRO 1445.02      1978
## 5        Y1    MICRO 1683.17      1979
## 6        Y1    MICRO 2038.15      1980
head(yearly_fs)
##   series_id method timestamp origin_timestamp forecast     lo80     hi80
## 1        Y1  ARIMA      1989             1988  5486.10 5363.602 5608.598
## 2        Y1  ARIMA      1990             1988  6035.21 5761.297 6309.123
## 3        Y1  ARIMA      1991             1988  6584.32 6125.975 7042.665
## 4        Y1  ARIMA      1992             1988  7133.43 6462.482 7804.378
## 5        Y1  ARIMA      1993             1988  7682.54 6774.072 8591.008
## 6        Y1  ARIMA      1994             1988  8231.65 7063.095 9400.205
##       lo90     hi90     lo95      hi95 horizon
## 1 5328.876 5643.324 5298.756  5673.444       1
## 2 5683.646 6386.774 5616.295  6454.125       2
## 3 5996.041 7172.599 5883.342  7285.298       3
## 4 6272.277 7994.583 6107.303  8159.557       4
## 5 6516.534 8848.546 6293.158  9071.922       5
## 6 6731.826 9731.474 6444.500 10018.800       6

1.2 plotHitrateChart

library(forvision)
plotHitrateChart(ts = yearly_ts, fs = yearly_fs, pi = 80)

library(forvision)
plotHitrateChart(ts = yearly_ts, fs = yearly_fs, h = 1, pi = 80)

library(forvision)
plotHitrateChart(ts = yearly_ts, fs = yearly_fs, pi = 90)

library(forvision)
plotHitrateChart(ts = yearly_ts, fs = yearly_fs, m ="ETS", h = 1, pi = 90)

library(forvision)
plotHitrateChart(ts = yearly_ts, fs = yearly_fs, pi = 95)

2 Other PIs data

2.1 Load data:

other_ts <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_other_TSTS.csv", stringsAsFactors = FALSE)
other_fs <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_other_PIs_FTS.csv", stringsAsFactors = FALSE)
head(other_ts)
##   series_id category   value timestamp
## 1        O1    MICRO 3060.42         1
## 2        O1    MICRO 3021.19         2
## 3        O1    MICRO 3301.13         3
## 4        O1    MICRO 3287.03         4
## 5        O1    MICRO 3080.71         5
## 6        O1    MICRO 3160.68         6
head(other_fs)
##   series_id method timestamp origin_timestamp forecast     lo80     hi80
## 1        O1  ARIMA        97               96 4571.389 4408.975 4733.803
## 2        O1  ARIMA        98               96 4531.236 4261.024 4801.447
## 3        O1  ARIMA        99               96 4558.815 4216.000 4901.630
## 4        O1  ARIMA       100               96 4550.705 4159.840 4941.570
## 5        O1  ARIMA       101               96 4544.681 4098.461 4990.900
## 6        O1  ARIMA       102               96 4555.453 4067.195 5043.710
##       lo90     hi90     lo95     hi95 horizon
## 1 4362.933 4779.845 4322.998 4819.780       1
## 2 4184.423 4878.048 4117.983 4944.489       2
## 3 4118.816 4998.814 4034.524 5083.106       3
## 4 4049.035 5052.375 3952.928 5148.482       4
## 5 3971.964 5117.397 3862.247 5227.115       5
## 6 3928.781 5182.124 3808.727 5302.178       6

2.2 plotHitrateChart

library(forvision)
plotHitrateChart(ts = other_ts, fs = other_fs, pi = 80)

library(forvision)
plotHitrateChart(ts = other_ts, fs = other_fs, h = 1:3, pi = 80)

library(forvision)
plotHitrateChart(ts = other_ts, fs = other_fs, pi = 90)

library(forvision)
plotHitrateChart(ts = other_ts, fs = other_fs, m = "ARIMA", h = 3:8, pi = 80)

library(forvision)
plotHitrateChart(ts = other_ts, fs = other_fs, pi = 95)