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I am working on a revenue and sales time series data, and I am trying to find the best forecast model. The data is daily for about 4 years and there are multiple seasonality in the data. I have used ARIMA, exponential smoothing, TS decomposition and a dummy regression models so far. But I am still not sure if these models are the best choices. I was wondering if there are better models which are suitable for the daily data with intense seasonality? Is there any machine learning techniques which can handle that?

I greatly appreciate your insight on that.

Ali

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You might want to look at the "forecast" package in R.

https://www.otexts.org/fpp

https://www.youtube.com/watch?v=1Lh1HlBUf8k

Dave

Thanks Dave, The first website was very good. I have used auto.arima and ets functions from the forecast package. Both take a rather long time to run, as my data has multiple seasonalities. I was wondering if there is any machine learning algorithm which can handle such cases faster? I am thinking about Neural Network, but not sure if it's the best approach... 

You can also go through a method called FORSYS developed by Rudolf Lewandowski http://onlinelibrary.wiley.com/doi/10.1002/for.3980010206/abstract

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