Time Series Prediction Using LSTM Deep Neural Networks

Post by digitalzombie » Tue, Sep 04, 2018, 12:25 PM UTC

sidr wrote: post #69

srean wrote: post #54

Earlier quoted context omitted.

> What are the statistical equivalents for this? That would be nonparametric statistics.

> That would be nonparametric statistics. No, it wouldn't. Firstly, nonparametrics in general can be a little misleading. The most common instantiations place function ("process") priors on modeling decisions that are otherwise found through trial and error. Those process priors do have their own parameters though. But more importantly, LSTMs and neural networks are very much parametric - their success come from the…

Your CS term for parametric is not quite 1 to 1 with statistic usage for parametric.

Also what you're describing is very similar to Bayesian statistic.

> But more importantly, LSTMs and neural networks are very much parametric - their success come from the advances in computing and optimization that have enabled estimating these parameters in very complicated model structures.

Which for statistician is basically blackbox and nonparametric since you have no idea what the distribution is dude and there is no assumption of a distribution. Hence nonparametric statistic which is the answer to your question you've asked for.

Post by digitalzombie » Wed, Sep 05, 2018, 4:43 AM UTC

md2be wrote: post #48

zwaps wrote: post #28

I find it interesting that Computer Scientists are basically rediscovering statistics. Now when predicting time series, an issue is that most model (like ARIMA, GARCH etc.) are short-memory processes. When you look at the full-series prediction of LSTMs, you observe the same thing. So in terms of Time Series, Machine Learning is currently in the mid to late 80's compared to Financial Econometrics. So if you are a CS,…

I agree with your sentiments, but there is a contribution that the CS departments made that the statistics, math, Econ (as in econometrics departments) seemed to have overlooked. I remember going to each of these departments in 2002 and asking them why don’t we split the data sets to train and update the coefficients and automate the process. The answer was always the same “that’s trivial and adds nothing to the fiel…

> why don’t we split the data sets to train and update the coefficients and automate the process.

What you just stated is just a pipeline. You can just split the data and train it and automate with tree ensemble that aren't boosting that is if you're talking about doing in parallel.

If you're just saying split and do as batch process in different time interval you can do that with nonparametric bayesian.

CS contribution in creating Deep learning and having it be the best accurate algo for certain data domain is pretty nice. But again Stat care a lot more than prediction.

Re: Time Series Prediction Using LSTM Deep Neural Networks

Re: Time Series Prediction Using LSTM Deep Neural Networks