Forecasting a point process with an ARIMA model

Document Type

Article

Publication Date

1-1-2016

Publication Title

Communications in Statistics - Theory and Methods

Volume

45

Issue

17

First page number:

5066

Last page number:

5076

Abstract

In fitting a power-law process, we show that the construction of the empirical recurrence rate time series either simplifies the modeling task, or liberates a point process restrained by a key parametric model assumption such as the monotonicity requirement of the intensity function. The technique can be applied to seasonal events occurring in spurts or clusters, because the autoregressive integrated moving average (ARIMA) procedure provides a comprehensive set of tools with great flexibility. Essentially, we consolidate two of the most powerful modeling tools for the stochastic process and time series in the statistical literature to handle counts of events in a Poisson or Poisson-like process. © 2016, © Taylor & Francis Group, LLC.

Keywords

Empirical recurrence rate; ERR-plot; Power-law process; Time series

Language

English

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