I have a time series of data points. Then I am given a future timestamp and  I have to predict the value for the data point. For simplicity, you can assume that the timestamp is bounded i.e. for e.g. query can be of at most 1 hr in the future. This is different from tradition train and predict models. Here you will be given the time as query input apart from past data.Currently, I am training different models for each minute(yeah, 60 models: lots of waiting time ).I am wondering if there is something available for this specific task?EDIT: To get the view about the data, you can assume a simple time series of real numbers, and I have to use history to predict for any general time(within next 1 hr).
Series prediction for any given time
machine learning;deep learning;time series
Considering ocam's razor I would recommend to use the simplest model first and increase the complexity if the simple models fail:Exponentially Weighted Moving Average, allows only for auto correlation with lag oneARIMAX, allows for several lagged autocorrelation, seasonal adjustments and external regressor.Fourier transformation, allows to fit more shapes time series but they are often more complex to explain to usersAll these models can predict one step ahead and you can repeat this up to 60. Of course the confidence intervals become much wider these many steps ahead. Base R has many time series available. These two models work well for many time series data. I would only start to use neural nets when these are not working or if you are looking for say edge detection. Neural netsIf neural nets are the only option to go you can check this post or if you have matlab available this post. I recently found this very interesting article on medium that explains how to fit a Neural Net to time series data. A more detailed implementation guide can be found here. I have not tried either approaches but I thought that the may be useful for future reference.Time series modus operandiNote that in any case, before staring off modeling your time series it is highly recommended to take some preparatory steps:Make your series stationary as explained hereUnderstand what elements you can decompose in your series as explained hereHope this helps