Online Learning for Time-Series
In this chapter, we are going to dive into online learning and streaming data for time-series. Online learning means that we continually update our model as new data is coming in. The advantage of online learning algorithms is that they can handle the high speed and possibly large size of streaming data and are able to adapt to new distributions of the data.
We will discuss drift, which is important because the performance of a machine learning model can be strongly affected by changes to the dataset to the point that a model will become obsolete (stale).
We are going to discuss what online learning is, how data can change (drift), and how adaptive learning algorithms combine drift detection methods to adjust to this change in order to avoid the degradation of performance or costly retraining.
We're going to cover the following topics:
- Online learning for time-series
- Online algorithms
