"Again time elapsed." | ||
| --Carolyn Keene, The Secret of the Old Clock | ||
In several of the previous chapters, we saw how we can apply iterative algorithms to identify solutions to complex equations. We first encountered this with gradient descent—both batch and stochastic—but most recently we saw it in community detection in graphs using the graph-parallel model of computation.
This chapter is about time series data. A time series is any data series that consists of regular observations of a quantity arranged according to the time of their measurement. For many of the techniques in this chapter to work, we require that the intervals between successive observations are all equal. The period between measurements could be monthly in the case of sales figures, daily in the case of rainfall or stock market fluctuations, or by minute in the case of hits to a high-traffic website.
For us to be able to predict the future values of a time series, we require that the future values...