The problem that I am dealing with is predicting time series values. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values. So far I have come across two models:

* [LSTM](http://deeplearning.net/tutorial/lstm.html) (long short term memory; a class of recurrent neural networks)
* ARIMA

I have tried both and read some articles on them. Now I am trying to get a better sense on how to compare the two. What I have found so far:

1. LSTM works better if we are dealing with huge amount of data and enough training data is available, while ARIMA is better for smaller datasets (is this correct?)
2. ARIMA requires a series of parameters `(p,q,d)` which must be calculated based on data, while LSTM does not require setting such parameters. However, there are some hyperparameters we need to tune for LSTM.
3. **EDIT:** One major difference between the two that I noticed while reading a great article [here](http://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/), is that ARIMA could only perform well on stationary time series (where there is no seasonality, trend and etc.) and you need to take care of that if want to use ARIMA

Other than the above-mentioned properties, I could not find any other points or facts which could help me toward selecting the best model. I would be really grateful if someone could help me finding articles, papers or other stuff (had no luck so far, only some general opinions here and there and nothing based on experiments.)

I have to mention that originally I am dealing with streaming data, however for now I am using [NAB datasets](https://github.com/numenta/NAB) which includes 50 datasets with the maximum size of 20k data points.