# predicting probability distribution for time series

I have time series of several variables. Just in one specific case one variable is linear combination of the rest.

I want to predict probability distribution (that is not only best estimate but estimates with probability of that happening) of future value of variables. I want to see when probability of small interval of possible outcome is high.

A priory I don't know rules of the game how variables evolve and inter-depend.

What is the tool to best do such prediction and how easy is it? Will scikit-learn do? Maybe neural networks?

As I've understood, time series theory data science assumes random walk, however I assume variables are at least partly moved by free will of players of the game under some a priory unknown to me constraints and goals.

Should statistical solution advised work in such case? Can data predict reversal of possible current trend?

• Welcome to DataScience.SE! Look into Gaussian process regression and Bayesian time series forecasting. – Emre Jul 14 '16 at 0:37
• @Alexei Pls explain what you mean by "small interval of possible outcome". This question way too broad. You need to provide more description of the process from which you collected this data. – horaceT Jan 8 '17 at 4:16

• @horaceT the original question was: What is the tool to best do such prediction and how easy is it? Will scikit-learn do? Maybe neural networks? I replied by saying Yes, you can use scikit-learn for this use case.. I further added a link to a tutorial which shows how probability distribution for a time series may be predicted. I do not see how this is not an answer. I am not being rude, just trying to see how I may improve :) – Shagun Sodhani Jan 8 '17 at 6:02