Time series, ranepa, spring 2020

2020_02_19_week_02_start_with_rw_model_group_a.R

library(fable)
library(feasts)
library(lubridate)
library(tidyverse)

AirPassengers
class(AirPassengers) # старый добрый ts формат
AirPassengers

ap = as_tsibble(AirPassengers) # переделали в стильный tsibble формат

# график
autoplot(ap, value) + 
  labs(x = "Период", 
       y = "Число пассажиров (тыс чел/мес)") + 
  ggtitle("Пассажиропоток")

# отбор наблюдений 
ap2 = filter(ap, index > "1952-04-07")

# создание нового ряда:
ap3 = mutate(ap, log_pax = log(value),
             pax2 = value^2,
             pax_1 = lag(value))
head(ap3)
autoplot(ap3, log_pax)

mtable = model(ap, 
  rand_walk = NAIVE(value),
  ma_1 = ARIMA(value ~ pdq(0, 0, 1) + PDQ(0, 0, 0)),
  ets = ETS(value))
mtable

mtable$rand_walk[[1]]
mtable$ma_1[[1]]

fcst = forecast(mtable, h = "2 years")
autoplot(fcst, ap)
fcst

2020_02_19_week_02_start_with_rw_model_group_b.R

library(fable)
library(feasts)
library(tidyverse)

AirPassengers
?AirPassengers
class(AirPassengers)

ap = as_tsibble(AirPassengers)
ap

autoplot(ap, value)

# создание новых переменных
ap2 = mutate(ap, log_pax = log(value),
                 pax2 = value^2,
                 pax_1 = lag(value),
                 d_pax = value - lag(value))
head(ap2)

# усечение куска ряда
ap3 = filter(ap, index > "1953-06-20")
head(ap3)

mtable = model(ap,
            rand_walk = NAIVE(value),
            ets = ETS(value))
mtable$rand_walk[[1]]

fcst = forecast(mtable, h = "2 years")
fcst

autoplot(fcst, ap)

2020_03_04_ar1_ma1_ets.R

library(fable)
library(feasts)
library(tidyverse)
devtools::install_github("tidyverts/fable")

air = as_tsibble(AirPassengers)
glimpse(air)

autoplot(air, value)

air_train = filter(air, index < "1959-01-01")

models = model(air_train, 
               ar1 = ARIMA(value ~ pdq(1, 0, 0) + PDQ(0, 0, 0)),
               ma1 = ARIMA(value ~ pdq(0, 0, 1) + PDQ(0, 0, 0)),
               ann = ETS(value ~ error("A") + trend("N") + season("N")),
               aaa = ETS(value ~ error("A") + trend("A") + season("A")),
               ets = ETS(value))

fcst = forecast(models, h = "2 years") 
fcst               
fcst %>% filter(.model == "ar1") %>% autoplot(air)
accuracy(fcst, air)