Hello Angelo It seems to be an interesting use case for Ignite.
However, you should consider what Ignite is, and what is not. Essentially, Ignite is a distributed in-memory database/cache/grid/etc... It also has some distributed computing API capabilities. You can store data easily in Ignite, and consume data by your code written in Java. You can also use Python since there is a Python Ignite Client available if it makes your time series analysis easier. You can also use the Ignite Computing API to execute code on your cluster https://ignite.apache.org/docs/latest/distributed-computing/distributed-computing but in this case I think Python is not supported. Cheers Gianluca Bonetti On Fri, 5 Jan 2024 at 08:52, Angelo Immediata <angelo...@gmail.com> wrote: > I'm pretty new to Apache Ignite > > > I asked this also on stackoverflow ( > https://stackoverflow.com/questions/77667648/apache-ignite-time-series-forecasting) > but I received no answer > > I need to make some forecasting analysis > > Basically I can collect data in Ignite in real time. Ignite will store > data in its own caches > > Now I need to make some forecasting showing me the distribution of data in > the next X months/years by starting from observed and collected data. > > As far as I know, this kind of forecasting can be realized by time series > forecasting. In Ignite I see no time series based algorithm. Am I right? > > If I'm correct I may use or tensor flow or Deep Java Library. But in this > case what I don't understand is: where should I use these libraries? Inside > my thick client microservice or should I write an Ignite plugin in order to > use the scalability feature provided by Ignite? > > Thank you > > Angelo >
