Event series prediction via non-homogeneous Poisson process modelling
Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature to the regularly sampled time series traditionall...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/52161/ |
| _version_ | 1848798661822119936 |
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| author | Goulding, James Preston, Simon P. Smith, Gavin |
| author_facet | Goulding, James Preston, Simon P. Smith, Gavin |
| author_sort | Goulding, James |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature to the regularly sampled time series traditionally the concern of hard sciences. As mass sets of such data have become more common, so interest in predicting future events in them has grown. Yet repurposing of traditional forecasting approaches has proven ineffective, in part due to issues such as sparsity, but often due to inapplicable underpinning assumptions such as stationarity and ergodicity.
In this paper we derive a principled new approach to forecasting event series that avoids such assumptions, based upon: 1. the processing of event series datasets in order to produce a parameterized mixture model of non-homogeneous Poisson processes; and 2. application of a technique called parallel forecasting that uses these processes’ rate functions to directly generate accurate temporal predictions for new query realizations. This approach uses forerunners of a stochastic process to shed light on the distribution of future events, not for themselves, but for realizations that subsequently follow in their footsteps. |
| first_indexed | 2025-11-14T20:23:19Z |
| format | Conference or Workshop Item |
| id | nottingham-52161 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:23:19Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-521612020-05-04T18:26:06Z https://eprints.nottingham.ac.uk/52161/ Event series prediction via non-homogeneous Poisson process modelling Goulding, James Preston, Simon P. Smith, Gavin Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature to the regularly sampled time series traditionally the concern of hard sciences. As mass sets of such data have become more common, so interest in predicting future events in them has grown. Yet repurposing of traditional forecasting approaches has proven ineffective, in part due to issues such as sparsity, but often due to inapplicable underpinning assumptions such as stationarity and ergodicity. In this paper we derive a principled new approach to forecasting event series that avoids such assumptions, based upon: 1. the processing of event series datasets in order to produce a parameterized mixture model of non-homogeneous Poisson processes; and 2. application of a technique called parallel forecasting that uses these processes’ rate functions to directly generate accurate temporal predictions for new query realizations. This approach uses forerunners of a stochastic process to shed light on the distribution of future events, not for themselves, but for realizations that subsequently follow in their footsteps. 2016-12-13 Conference or Workshop Item PeerReviewed Goulding, James, Preston, Simon P. and Smith, Gavin (2016) Event series prediction via non-homogeneous Poisson process modelling. In: 2016 IEEE International Conference on Data Mining (ICDM), 12-25 Dec 2016, Barcelona., Spain. https://ieeexplore.ieee.org/document/7837840/ 10.1109/ICDM.2016.0027 10.1109/ICDM.2016.0027 10.1109/ICDM.2016.0027 |
| spellingShingle | Goulding, James Preston, Simon P. Smith, Gavin Event series prediction via non-homogeneous Poisson process modelling |
| title | Event series prediction via non-homogeneous Poisson process modelling |
| title_full | Event series prediction via non-homogeneous Poisson process modelling |
| title_fullStr | Event series prediction via non-homogeneous Poisson process modelling |
| title_full_unstemmed | Event series prediction via non-homogeneous Poisson process modelling |
| title_short | Event series prediction via non-homogeneous Poisson process modelling |
| title_sort | event series prediction via non-homogeneous poisson process modelling |
| url | https://eprints.nottingham.ac.uk/52161/ https://eprints.nottingham.ac.uk/52161/ https://eprints.nottingham.ac.uk/52161/ |