HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic

Luiza Sayfullina, Magnus Westerlund, Kaj Mikael Bjork, Hannu T. Toivonen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Trends show the underlying structure of the time series data. Trend estimation is a commonly used tool for financial market movement prediction. In traditional approaches, such as Hodrick-Prescott (HP) and L1 filtering, the trend is considered as a smoothed version of the time-series, including rare significant hills that are smoothed in the same way as usual noise. The goal of this paper is to allow the estimated trend to be more complex and detailed in the intervals of significant changes while making a smooth estimate in all other parts. This will be our main criteria for trend estimation. We present a modified version of HP weighted heuristic that provides the best trend according to the abovementioned criteria. Gaussian Mixture Models (GMMs) on the preliminary estimated trend are used in the weighted HP heuristic to decrease the penalty in the objective function for turning-point intervals. We conducted a set of experiments on financial datasets and compared the results with those obtained from the standard HP filtering with weighted heuristic. The results indicate an improvement in the cycling component using our proposed criteria compared to the HP filtering approach.

Original languageEnglish
Title of host publicationInternational Conference on Tools with Artificial Intelligence
Pages989-996
Number of pages8
Volume2014-December
ISBN (Electronic)9781479965724
DOIs
Publication statusPublished - 12 Dec 2014
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Tools with Artificial Intelligence - Limassol, Cyprus
Duration: 10 Nov 201412 Nov 2014
Conference number: 26

Conference

ConferenceIEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTA
CountryCyprus
CityLimassol
Period10/11/201412/11/2014

Keywords

  • HP Trend
  • HP Weighted Heuristic
  • L1 Trend
  • Time-Series

Fingerprint Dive into the research topics of 'HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic'. Together they form a unique fingerprint.

Cite this