I'm trying to find whether there is any package in R that deals with multiple seasonality for ARIMA models and, if not, if there is any way of going through it.
I have an hourly series and would like to test seasonality for lags=c(24,7*24,30*24)
Many thanks in advance.
You can use TBATS model, and her's an example
Related
I am currently using the Marima package for R invented by Henrik Spliid in order to forecast multivariate time series with ARIMA.
Overview can be found here:
https://cran.r-project.org/web/packages/marima/marima.pdf
http://orbit.dtu.dk/files/123996117/marima.anv.talk.pdf
When using the Marima function, it is required to define both the order of AR(p) and MA(q) first.
My question is, how can I determine appropriate values for p and q?
I know when it comes to univariate ARIMA analysis, that auto.arima gives a good suggestion for p and q. However, when I use auto.arima for every single univariate time series I want to analyze, there are (slightly) different suggestions for each time series. (For example (2,2,1) for the first, (1,1,1) for the second and so on)
Since I want to analyze all of the time series combined in the multivariate ARIMA model and I only can choose one value for each p and q (if I understood it correctly), I wonder how I can choose those values the most accurate way.
Could I just try to run the model a couple times and see what values for p and q work best (e.g. by testing the residuals of the forecast)?
What are your suggestions?
I would appreciate any help!
I find 2 ways of training and testing hts and I am not sure which one is appropriate
1. In this question (link given)
MASE Extraction Hierarchical Data ('hts' and 'forecast' packages R)
you have divided the time series into train and test and applied hts
2. In this question (link provided)
Hierarchical Time Series
you are applying hts to the entire ts and then splitting into train and test.
My question is which of these is correct? please help
Also, I would like to know how can i check the accuracy of the forecast i make. That is, how far into the future can I determine that the forecast would statistically be appropriate using the hts model I choose to use. Any pointers to any articles or an example would be helpful. I would like to determine if i should forecast for 4,8, or 12 weeks ahead.
Thanks in advance
I'm relatively new to R programming, but I've been reading your blogs and posts in order to get up-to-date with the forecast package. However, I have been struggling with the effect of seasonality.
Take for example the simplest signal possible:
train <- ts(sin((2*pi)*seq(from=0, to=10, by=0.01)))
If I just try to forecast this signal with brute force, I get irrelevant results:
plot(forecast(train,h=20))
However, if I manually detect the seasonality as 100, and do the following:
train <- ts(sin((2*pi)*seq(from=0, to=10, by=0.01)),frequency=100)
plot(forecast(train))
I get excellent forecasting results.
I'm honestly very puzzled by these results, which obviously happen for more complex signals.
If I remember correctly, when you create the time series object you have to specify its frequency. That way, the forecast method will be able to detect the seasonal pattern. There are some other ways to detect seasonality, like trying the auto arima function and checking if it selects a seasonal model. Apart from visual exploration, of course.
Does anyone know if it is possible to use regressors with the TBATS model in the R forecast package?
From the help file it looks like you can add additional parameters for ARMA and since ARIMA takes regressors (i.e xreg = X ) I was wondering if I could pass regressors to TBATS if I use ARMA (using.arma.erros=TRUE) in my TBATS model.
Im still a pretty new R use so I would really appreciate any guidance.
No, that is not possible. The xreg argument will be used when selecting the order of the ARMA model for the errors, but then it will be ignored when doing the estimation and forecasting.
I think TBATS does not accept regressors but only one regressor and that is the time which may be days, hours, years etc
we have to fit about 2000 or odd time series every month,
they have very idiosyncratic behavior in particular, some are arma/arima, some are ewma, some are arch/garch with or without seasonality and/or trend (only thing in common is the time series aspect).
one can in theory build ensemble model with aic or bic criterion to choose the best fit model but is the community aware of any library which attempts to solve this problem?
Google made me aware of the below one by Rob J Hyndman
link
but are they any other alternatives?
There are two automatic methods in the forecast package: auto.arima() which will handle automatic modelling using ARIMA models, and ets() which will automatically select the best model from the exponential smoothing family (including trend and seasonality where appropriate). The AIC is used in both cases for model selection. Neither handles ARCH/GARCH models though. The package is described in some detail in this JSS article: http://www.jstatsoft.org/v27/i03
Further to your question:
When will it be possible to use
forecast package functions, especially
ets function, with high dimensional
data(weekly data, for example)?
Probably early next year. The paper is written (see robjhyndman.com/working-papers/complex-seasonality) and we are working on the code now.
Thanks useRs, I have tried the forecast package, that too as a composite of arima and ets, but not to much acclaim from aic or bic(sbc), so i am now tempted to treat each of the time series to its own svm(support vector machine) because of its better genralization adaptability and also being able to add other variables apart from lags and non linear kernel functions
Any premonitions?