Trying to wrap my head around the capabilities of CoreML.
Say I have a large arary of numbers on time domain, and I would like to predict and find patterns using CoreML. (find unknown patterns relate to time and magnitude)
For instance - LSTM Network , Random Forest, Time Delay, etc.
Most of the models in Apple docs, relate to image and text.
I have read this :
Which not seems to answer what I need.
Is there anything like this ?
Are there other Core tools for iOS ?
There are two ways you can work with time series:
Treat the time series as a static thing, for example every month consists of an input vector of 30 numbers (or 1000 numbers or whatever kind of data you have). You make a prediction based on that vector.
Use a sequence model like in Apple's link. These models (LSTM etc) keep track of state, which more-or-less remembers what they have seen in the past.
Which one you need to use depends on the actual problem you're trying to solve.