CODEWITHSUNDEEP
rtime-series
sh_student
sh_student

Reputation: 389

How to transform time series code to automated code to use for multiple time series?

I want to transform my time series code for one time series to an automated code which can be used for multiple time series data (my data contains a monthly time series). My general approach for one time series was to remove the seasonal component and take first differences to achieve stationarity. Then I use auto.arima to get the ARIMA parameters. I use these parameters to build my ARIMA model with my original time series data. Then I forecast and compare to the actual data of 4 months (which I have cut out before) and calculate the RMSE. As I cannot use my actual data, I just generate a random time series and test set as an example - of course the outcome does not make much sense. 

library('forecast')
set.seed(123)

# create random time series and 4 months testing data
ts <- ts(runif(26, min = 50, max = 3000), start = c(2017,01), end = c(2019,02), frequency = 12)
test.data <- runif(4, min = 50, max = 3000)

# Decomompose
comp.ts = decompose(ts)

# subtrect seasonal trend
ts2 <- ts - comp.ts$seasonal
ts2 <- diff(ts2, differences=1)

auto.arima(ts2, trace = T, seasonal = TRUE,ic = 'aicc', max.p = 10,max.q = 10,max.P = 10,max.Q = 10,max.d = 10, stepwise = F)

# Use auto.arima outcome as input
my.arima <- Arima(ts2, order=c(0,0,0),seasonal = list(order = c(0,1,0), period = 12),method="ML", include.drift = F)
# Forecast and calculate RMSE
data.forecast <- forecast(my.arima, h=4, level=c(99.5))
my.difference <- test.data - data.forecast$mean
my.rmse <- (sum(sqrt(my.difference^2)))/length(my.difference)

As my actual data set contains over 500 time series, I need to automate the whole process. Unfortunately, I have not used R for time series so far, so I have problems coming up with an automated process.

Lets assume 4 random time series with 4 random test sets. How could I generate an automated process for these time series (which I can also use for my actual 500+ time series) which does the exact same thing as above?

ts1 <- ts(runif(26, min = 50, max = 3000), start = c(2017,01), end = c(2019,02), frequency = 12)
ts2 <- ts(runif(26, min = 50, max = 3000), start = c(2017,01), end = c(2019,02), frequency = 12)
ts3 <- ts(runif(26, min = 50, max = 3000), start = c(2017,01), end = c(2019,02), frequency = 12)
ts4 <- ts(runif(26, min = 50, max = 3000), start = c(2017,01), end = c(2019,02), frequency = 12)
test.data1 <- runif(4, min = 50, max = 3000)
test.data2 <- runif(4, min = 50, max = 3000)
test.data3 <- runif(4, min = 50, max = 3000)
test.data4 <- runif(4, min = 50, max = 3000)

Thanks for the help!

Upvotes: 1

Views: 113

Answers (1)

jay.sf
jay.sf

Reputation: 72593

Just put your workflow into a function.

serialArima <- function(ts, test.data) {
  library(forecast)
  # Decomompose
  comp.ts=decompose(ts)

  # subtrect seasonal trend
  ts2 <- ts - comp.ts$seasonal
  ts2 <- diff(ts2, differences=1)

  auto.arima(ts2, trace=T, seasonal=TRUE, ic='aicc', max.p=0, max.q=0, max.P=0,
             max.Q=0, max.d=0, stepwise=F)

  # Use auto.arima outcome as input
  my.arima <- Arima(ts2, order=c(0, 0, 0),
                    seasonal=list(order=c(0, 1, 0), period=2), 
                    method="ML", include.drift=F)
  # Forecast and calculate RMSE
  data.forecast <- forecast(my.arima, h=4, level=c(99.5))
  my.difference <- test.data - data.forecast$mean
  my.rmse <- (sum(sqrt(my.difference^2)))/length(my.difference)
  return(list(data.forecast=data.forecast, my.difference=my.difference, my.rmse=my.rmse))
}

Singular application

serialArima(ts, test.data)
# ARIMA(0,0,0)           with zero mean     : 82.45803
# ARIMA(0,0,0)           with non-zero mean : 88.13593
# 
# 
# 
# Best model: ARIMA(0,0,0)           with zero mean     
# 
# $data.forecast
# Point Forecast   Lo 99.5  Hi 99.5
# 2020.00      -349.1424 -2595.762 1897.477
# 2020.50       772.6014 -1474.018 3019.221
# 2021.00      -349.1424 -3526.342 2828.057
# 2021.50       772.6014 -2404.598 3949.801
# 
# $my.difference
# Time Series:
#   Start = c(2020, 1) 
# End = c(2021, 2) 
# Frequency = 2 
# [1] 1497.2446  840.4139 2979.4553  993.5614
# 
# $my.rmse
# [1] 1577.669

Multiple application

Map(serialArima, list(ts1, ts2, ts3, ts4), 
    list(test.data1, test.data2, test.data3, test.data4))

Upvotes: 1

Related Questions