A Novel Decision Tree Approach for the Handling of Time Series

Brunello, Andrea; Marzano, Enrico; Montanari, Angelo; Sciavicco, Guido

doi:10.1007/978-3-030-05918-7_32

Cited by 3 publications

(1 citation statement)

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“…This paper presents J48SS, a novel decision tree learner based on WEKA's J48 (a Java implementation of C4.5 [1]) (preliminary conference versions of some of its contributions can be found in [2,3]). The algorithm is capable of naturally exploiting categorical, numerical, sequential, and time series data during the same execution cycle.…”

Section: Introductionmentioning

confidence: 99%

J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Brunello

Marzano²,

Montanari

et al. 2019

Computers

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Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

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Section: Introductionmentioning

confidence: 99%