I am new to machine learning and have started brainstorming some model ideas that involve financial instrument/time series data. I was thinking it might be useful to use a classification algorithm to predict if an instrument was in fact up or down y% (TRUE/FALSE) after n days, based on i.e. a combination of technical indicator states for each learning example.
That said, in researching the idea I came upon an article that stated training examples in time series data are not independent of each other:
"Time series data has a natural temporal ordering - this differs from typical data mining/machine learning applications where each data point is an independent example of the concept to be learned, and the ordering of data points within a data set does not matter"
My question is as follows: is the above true only if we are trying to predict the continuous value of an asset n days into the future? As far as my idea outlined in the first paragraph is concerned, would this then still be valid considering I am not (apparently) taking into account the specific relationship