ts<- read.csv("~/GitHub/forvision_data/example1_TSTS.csv")
fs <- read.csv("~/GitHub/forvision_data/example1_FTS.csv")
head(ts)## series_id value timestamp
## 1 Y1 3103.96 1984
## 2 Y1 3360.27 1985
## 3 Y1 3807.63 1986
## 4 Y1 4387.88 1987
## 5 Y1 4936.99 1988
## 6 Y1 5379.75 1989head(fs)## series_id method timestamp origin_timestamp forecast horizon lo90
## 1 Y1 A 1989 1988 5406.43 1 5183.349
## 2 Y1 A 1990 1988 5875.96 2 5652.879
## 3 Y1 A 1991 1988 6345.48 3 6122.399
## 4 Y1 B 1989 1988 5473.87 1 5250.789
## 5 Y1 B 1990 1988 6010.43 2 5787.349
## 6 Y1 B 1991 1988 6546.63 3 6323.549
## hi90
## 1 5629.511
## 2 6099.041
## 3 6568.561
## 4 5696.951
## 5 6233.511
## 6 6769.711plotFanChart(ts, fs, id = "Y1", m = "A", origin = 1988)Error in plotFanChart(ts, fs, id = “Y1”, m = “A”, origin = 1988) : Check the column names of input data frame ts. The input data ts needed in the form of a data frame containing columns named ‘series_id’, value, and ‘timestamp_dbo’.
timestamp_dbots$timestamp_dbo <- ts$timestamp
fs$timestamp_dbo <- fs$timestamp
plotFanChart(ts, fs, id = "Y1", m = "A", origin = 1988)requires an appropriate time-based object
library(zoo)
ts$timestamp_dbo <- as.yearmon(ts$timestamp)
fs$timestamp_dbo <- as.yearmon(fs$timestamp)
plotFanChart(ts, fs, id = "Y1", m = "A", origin = 1988)library(magrittr)
library(dplyr)
fs %<>% mutate(lo98 = lo90 + 50, hi98 = hi90 + 50, lo50 = lo90-80, hi50 = hi90-80, lo60 = lo90+120, hi60 = hi90 + 120)
head(fs)## series_id method timestamp origin_timestamp forecast horizon lo90
## 1 Y1 A 1989 1988 5406.43 1 5183.349
## 2 Y1 A 1990 1988 5875.96 2 5652.879
## 3 Y1 A 1991 1988 6345.48 3 6122.399
## 4 Y1 B 1989 1988 5473.87 1 5250.789
## 5 Y1 B 1990 1988 6010.43 2 5787.349
## 6 Y1 B 1991 1988 6546.63 3 6323.549
## hi90 timestamp_dbo lo98 hi98 lo50 hi50 lo60
## 1 5629.511 Jan 1989 5233.349 5679.511 5103.349 5549.511 5303.349
## 2 6099.041 Jan 1990 5702.879 6149.041 5572.879 6019.041 5772.879
## 3 6568.561 Jan 1991 6172.399 6618.561 6042.399 6488.561 6242.399
## 4 5696.951 Jan 1989 5300.789 5746.951 5170.789 5616.951 5370.789
## 5 6233.511 Jan 1990 5837.349 6283.511 5707.349 6153.511 5907.349
## 6 6769.711 Jan 1991 6373.549 6819.711 6243.549 6689.711 6443.549
## hi60
## 1 5749.511
## 2 6219.041
## 3 6688.561
## 4 5816.951
## 5 6353.511
## 6 6889.711fs1 <- fs[, 1:11]
head(fs1)## series_id method timestamp origin_timestamp forecast horizon lo90
## 1 Y1 A 1989 1988 5406.43 1 5183.349
## 2 Y1 A 1990 1988 5875.96 2 5652.879
## 3 Y1 A 1991 1988 6345.48 3 6122.399
## 4 Y1 B 1989 1988 5473.87 1 5250.789
## 5 Y1 B 1990 1988 6010.43 2 5787.349
## 6 Y1 B 1991 1988 6546.63 3 6323.549
## hi90 timestamp_dbo lo98 hi98
## 1 5629.511 Jan 1989 5233.349 5679.511
## 2 6099.041 Jan 1990 5702.879 6149.041
## 3 6568.561 Jan 1991 6172.399 6618.561
## 4 5696.951 Jan 1989 5300.789 5746.951
## 5 6233.511 Jan 1990 5837.349 6283.511
## 6 6769.711 Jan 1991 6373.549 6819.711plotFanChart(ts, fs1, id = "Y1", m = "A", origin = 1988)fs2 <- fs[, 1:13]
head(fs2)## series_id method timestamp origin_timestamp forecast horizon lo90
## 1 Y1 A 1989 1988 5406.43 1 5183.349
## 2 Y1 A 1990 1988 5875.96 2 5652.879
## 3 Y1 A 1991 1988 6345.48 3 6122.399
## 4 Y1 B 1989 1988 5473.87 1 5250.789
## 5 Y1 B 1990 1988 6010.43 2 5787.349
## 6 Y1 B 1991 1988 6546.63 3 6323.549
## hi90 timestamp_dbo lo98 hi98 lo50 hi50
## 1 5629.511 Jan 1989 5233.349 5679.511 5103.349 5549.511
## 2 6099.041 Jan 1990 5702.879 6149.041 5572.879 6019.041
## 3 6568.561 Jan 1991 6172.399 6618.561 6042.399 6488.561
## 4 5696.951 Jan 1989 5300.789 5746.951 5170.789 5616.951
## 5 6233.511 Jan 1990 5837.349 6283.511 5707.349 6153.511
## 6 6769.711 Jan 1991 6373.549 6819.711 6243.549 6689.711plotFanChart(ts, fs2, id = "Y1", m = "A", origin = 1988)