ARIMA forecasting with auto.Arima() and xreg
By : Jenny
Date : March 29 2020, 07:55 AM
I wish did fix the issue. A few points. One, you can just convert the entire matrix to a ts object and then isolate the variables later. Second, if you are using covariates in your arima model then you will need to provide them when you forecast outofsample. This may mean forecasting each of the covariates before generating forecasts for your variable of interest. In the example below I split the data into two samples for simplicity. code :
dta = read.csv("xdata.csv")[1:96,]
dta < ts(dta, start = 1)
# to illustrate out of sample forecasting with covariates lets split the data
train < window(dta, end = 90)
test < window(dta, start = 91)
# fit model
covariates < c("DayOfWeek", "Customers", "Open", "Promo", "SchoolHoliday")
fit < auto.arima(train[,"Sales"], xreg = train[, covariates])
# forecast
fcast < forecast(fit, xreg = test[, covariates])

ARIMA forecasting with auto.Arima() and xreg problem
By : Betsabe Segura
Date : March 29 2020, 07:55 AM
will be helpful for those in need Analysing the code of the auto.arima function, it is easy to find the code used for checking the rank deficiency of matrices. I suggest the following function: code :
is.rankdeficient < function(xregg) {
constant_columns < apply(xregg, 2, is.constant)
if (any(constant_columns)) {
xregg < xregg[, which(constant_columns)[1]]
}
sv < svd(na.omit(cbind(rep(1, NROW(xregg)), xregg)))$d
min(sv)/sum(sv) < .Machine$double.eps
}
is.rankdeficient(PP)
# [1] FALSE
is.rankdeficient(NN)
# [1] TRUE

A fast solution to obtain the best ARIMA model in R (function `auto.arima`)
By : Rose
Date : March 29 2020, 07:55 AM
Hope this helps The forecast package has many of its functions built with parallel processing in mind. One of the arguments of the auto.arima() function is 'parallel'. According to the package documentation, "If [parallel = ] TRUE and stepwise = FALSE, then the specification search is done in parallel.This can give a significant speedup on mutlicore machines."

auto arima: r and python suggest different arima models for same data, why?
By : user2925041
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I have tried using auto arima in python at the same time on R for the same data but got different ARIMA model selection being the best model with different AIC. Can you tell me why I am getting different best models with different AIC from the two languages? , I have moved around the web and found this python code very useful code :
# import package
import itertools
# Define the p, d and q parameters to take any value between 0 and 2
p = d = q = range(0, 3)
# Generate all different combinations of p, q and q triplets
pdq = list(itertools.product(p, d, q))
# Generate all different combinations of seasonal p, q and q
triplets
seasonal_pdq = [(x[0], x[1], x[2], 12) for x in
list(itertools.product(p, d, q))]
print('Examples of parameter combinations for Seasonal ARIMA...')
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))
warnings.filterwarnings("ignore") # specify to ignore warning messages
for param in pdq:
for param_seasonal in seasonal_pdq:
try:
mod = sm.tsa.statespace.SARIMAX(ts,
order=param,
seasonal_order=param_seasonal,
enforce_stationarity=False,
enforce_invertibility=False)
results = mod.fit()
print('ARIMA{}x{}12  AIC:{}'.format(param, param_seasonal, results.aic))
except:
continue

in R, how to capture ARIMA elements from auto.arima results
By : Rup
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further You can try summary(arima.model), arima.model$coef, arima.model$aic, arima.model$bic. If you want a tidy format, you can use broom package like this: code :
library(broom)
tidy(arima.model) #ar/ma terms
glance(arima.model) #information criteria
tidy(arima.model)
# A tibble: 3 x 3
term estimate std.error
<fct> <dbl> <dbl>
1 ar1 0.959 0.0380
2 ma1 1.73 0.0745
3 ma2 0.774 0.0658
glance(arima.model)
# A tibble: 1 x 4
sigma logLik AIC BIC
<dbl> <dbl> <dbl> <dbl>
1 0.256 6.10 20.2 31.5

