1 Өгөгдөл

library(highcharter)
library(stats)
library(forecast)
library(xts)
library(dplyr)
library(hms)
library(lubridate)
inflation<- read.csv("~/Desktop/inf.csv")

2 Өгөгдлийг хувиргах

c<-ts(inflation$Инфляци, start=c(2007,1), freq=12)
c
##       Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec
## 2007  5.2  6.4  7.2  6.0  5.7  5.9  6.4 10.8 12.6 14.7 16.7 17.8
## 2008 19.9 21.0 24.0 29.4 33.9 33.6 33.0 34.2 32.2 27.9 24.2 22.1
## 2009 20.7 18.2 16.3 12.5  8.0  6.3  4.9  0.6  0.0  0.9  3.5  4.2
## 2010  5.7  8.3  8.5  8.3 11.6 11.4  9.8 11.2 10.6 11.3 11.1 13.0
## 2011 13.8 11.0  8.0  5.5  4.2  6.2 10.1  9.0 10.5 10.9 10.8 10.2
## 2012 10.2 12.4 15.3 16.0 15.4 14.7 14.5 14.9 14.8 15.0 14.4 14.0
## 2013 12.6 10.9  9.3  9.9  9.2  8.3  7.8  8.8  9.1 10.2 11.4 11.9
## 2014 12.3 12.2 12.4 12.3 13.7 14.6 14.9 13.7 13.0 12.1 11.5 11.0
## 2015  9.8  9.3  9.3  9.2  8.0  7.3  6.9  6.6  4.9  3.4  2.9  1.9
## 2016  0.6  0.8  0.8  1.3  1.3  1.2  1.5 -0.1  0.0 -0.2  0.5  1.3
## 2017  2.1  2.4  3.1  3.2  3.6  3.4  3.4  5.0  5.8  6.9  6.5  6.4
## 2018  6.9  6.9  6.6  6.0  6.1  7.2  7.7  6.0  5.7  6.3  8.1

3 ARIMA hchart график

hchart(c)
c %>% 
  forecast(level = 90) %>% 
  hchart()
head(as.xts(c))
##          [,1]
## Jan 2007  5.2
## Feb 2007  6.4
## Mar 2007  7.2
## Apr 2007  6.0
## May 2007  5.7
## Jun 2007  5.9

4 Autocorrelation функц график

x <- acf(diff(c), plot = FALSE)
hchart(x)

5 Өгөгдлийг xts хэлбэрт шилжүүлэх

data <- read.csv("~/downloads/BreadBasket.csv") 
p2<-data %>% 
  group_by(Date) %>% 
  summarise(Count= n()) %>% 
  mutate(Day=wday(Date,label=T))
p2$Date <- as.POSIXct(p2$Date, format("%Y-%m-%d"))
D <- xts(p2[, 2], order.by = p2$Date)
head(D)
##            Count
## 2016-10-30   180
## 2016-10-31   205
## 2016-11-01   154
## 2016-11-02   169
## 2016-11-03   195
## 2016-11-04   192
hchart(D)