logistic regression的一些问题，不平衡数据，时间序列，求解惑

Q:　　I would like to use a binary logistic regression model in the context of streaming data (multidimensional time series) in order to predict the value of the dependent variable of the data (i.e. row) that just arrived, given the past observations. As far as I know, logistic regression is traditionally used for postmortem analysis, where each dependent variable has already been set (either by inspection, or by the nature of the study).

A:　　There are two methods to consider:

Only use the last N input samples. Assuming your input signal is of dimension D, then you have N*D samples per ground truth label. This way you can train using any classifier you like, including logistic regression. This way, each output is considered independent from all other outputs.

Use the last N input samples and the last N outputs you have generated. The problem is then similar to viterbi decoding. You could generate a non-binary score based on the input samples, and combine the score of multiple samples using a viterbi decoder. This is better than method 1. if you now something about the temporal relation between the outputs.