Tracking the Evolution of Financial Time Series Clusters - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2019

Tracking the Evolution of Financial Time Series Clusters

Davide Azzalini
  • Function : Author
  • PersonId : 1051299
Fabio Azzalini
  • Function : Author
  • PersonId : 1051300

Abstract

Nowadays, a huge amount of applications exist that natively adopt a data-streaming model to represent highly dynamic phenomena. A challenging application is constituted by data from the stock market, where the stock prices are naturally modeled as data streams that fluctuate very much and remain meaningful only for short amounts of time. In this paper we present a technique to track evolving clusters of financial time series, with the aim of constructing reliable models for this highly dynamic application. In our technique the clustering over a set of time series is iterated over time through sliding windows and, at each iteration, the differences between the current clustering and the previous one are studied to determine those changes that are "significant" with respect to the application. For example, in the financial domain, if a company that has belonged to the same cluster for a certain amount of time moves to another cluster, this may be a signal of a significant change in its economical or financial situation.
Fichier principal
Vignette du fichier
DSMM2019 (1).pdf (755.3 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02191810 , version 1 (23-07-2019)

Identifiers

  • HAL Id : hal-02191810 , version 1

Cite

Letizia Tanca, Davide Azzalini, Fabio Azzalini, Mirjana Mazuran. Tracking the Evolution of Financial Time Series Clusters. DSMM 2019 - 5th Workshop on Data Science for Macro-modeling with Financial and Economic Datasets, Jun 2019, Amsterdam, Netherlands. ⟨hal-02191810⟩
79 View
880 Download

Share

Gmail Facebook X LinkedIn More