Time Series Models

In this chapter we introduce the time series models in machine learning. These models are different in principle compared to most other models in the fact that the data that they work on is dynamic and is changing as a function of time. We will study some non-probabilistic techniques that are used to process such data including ARMA, ARIMA along with probabilistic techniques like hidden Markov models (HMM) and conditional random fields (CRF). Handling dynamic data is fundamentally different from dealing with static data, and needs a whole new perspective. In some cases dynamic data can be handled as static by taking snapshots of the data at specific times. However, this approach can only help to certain extent, and in order to solve the problems in dynamic data ultimately user needs to employ one of the techniques described here or deep neural networks as described in chapter dedicated on that topic.