I'm developing a log analyzer to predict and find errors in an equipament. Each logged data contains the following format:  Timestamp | Log source | type of message | messageEach entry log I want to represent by one pixel RGB because, considering the 24 bits, is possible to represent the last 3 parameters ( Log source, type of message and message), but I don't have bits enought to represent the timestamp ( this data I will to represent by the diference o previous timestamp delta of time), the resolution of time is second, and sometimes we have a large time between one log of another. The example above illustrate the situation:This project has the purpose of analyze the log to find and predict errors, and this preprocessing is used to simplfy the data entry to the machine learning algorithm, this is a good way to represent data to an rnn ? Or for this kind of problem exist a better way to make an analyses?
Preprocessing of training dataset for machine learning
recurrent neural networks;datasets;prediction