1 Load data:
ts <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_other_TSTS.csv", stringsAsFactors = FALSE)
fs <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_other_FTS.csv", stringsAsFactors = FALSE)
head(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(fs)
## series_id category method forecast horizon timestamp origin_timestamp
## 1 O1 MICRO NAIVE2 4542.51 1 97 96
## 2 O1 MICRO NAIVE2 4542.51 2 98 96
## 3 O1 MICRO NAIVE2 4542.51 3 99 96
## 4 O1 MICRO NAIVE2 4542.51 4 100 96
## 5 O1 MICRO NAIVE2 4542.51 5 101 96
## 6 O1 MICRO NAIVE2 4542.51 6 102 96
2 Добавление и преобразование колонки timestamp_dbo в виде date-based object:
library(zoo)
ts$timestamp_dbo <- as.yearmon(ts$timestamp)
fs$timestamp_dbo <- as.yearmon(fs$timestamp)
head(ts)
## series_id category value timestamp timestamp_dbo
## 1 O1 MICRO 3060.42 1 Jan 0001
## 2 O1 MICRO 3021.19 2 Jan 0002
## 3 O1 MICRO 3301.13 3 Jan 0003
## 4 O1 MICRO 3287.03 4 Jan 0004
## 5 O1 MICRO 3080.71 5 Jan 0005
## 6 O1 MICRO 3160.68 6 Jan 0006
head(fs)
## series_id category method forecast horizon timestamp origin_timestamp
## 1 O1 MICRO NAIVE2 4542.51 1 97 96
## 2 O1 MICRO NAIVE2 4542.51 2 98 96
## 3 O1 MICRO NAIVE2 4542.51 3 99 96
## 4 O1 MICRO NAIVE2 4542.51 4 100 96
## 5 O1 MICRO NAIVE2 4542.51 5 101 96
## 6 O1 MICRO NAIVE2 4542.51 6 102 96
## timestamp_dbo
## 1 Jan 0097
## 2 Jan 0098
## 3 Jan 0099
## 4 Jan 0100
## 5 Jan 0101
## 6 Jan 0102
3 plotFixedOrigin
library(forvision)
plotFixedOrigin(ts = ts, id ="O1")
plotFixedOrigin(ts = ts, fs = fs, id ="O1", origin = 96, m = c("DAMPEN", "NAIVE2", "HOLT"))
4 plotFanChart
fs2 <- read.csv("https://raw.githubusercontent.com/forvis/forvision_data/master/M3_other_PIs_FTS.csv", stringsAsFactors = FALSE)
head(fs2)
## 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
## lo95 hi95
## 1 4322.998 4819.780
## 2 4117.983 4944.489
## 3 4034.524 5083.106
## 4 3952.928 5148.482
## 5 3862.247 5227.115
## 6 3808.727 5302.178
- Добавление и преобразование колонки timestamp_dbo в виде date-based object:
library(zoo)
fs2$timestamp_dbo <- as.yearmon(fs2$timestamp)
library(forvision)
plotFanChart(ts = ts, fs = fs2, id ="O1", origin = 96, m = "ARIMA")