Adaptive sliding window algorithm for weather data segmentation

Yahyia BenYahmed, Azuraliza Abu Bakar, Abdul Razak Hamdan, Almahdi Ahmed, Sharifah Mastura Syed Abdullah

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Data segmentation is one of the primary tasks of time series mining. This task is often used to generate interesting subsequences from a large time series sequence. Segmentation is one of the essential components in extracting significant patterns of weather time series data, which may be useful in identifying the trend and changes in weather prediction. The task use interpolation to approximate the signal with a best-fitting series and return the last point of the segments as change point or as a sequence of time points as a window. Sliding window algorithm (SWA) is a well-known time series data segmentation method, in which a segment with an error threshold and fixed window size is created when the change point is reached. In actual data such as weather data, SWA is unsuitable because appropriate error threshold and change point are required to avoid information loss. In this paper, we propose an adaptive sliding window algorithm (ASWA) that categorizes weather time series data based on the change point information.

Original languageEnglish
Pages (from-to)322-333
Number of pages12
JournalJournal of Theoretical and Applied Information Technology
Volume80
Issue number2
Publication statusPublished - 20 Oct 2015

Fingerprint

Sliding Window
Change Point
Weather
Time series
Segmentation
Time Series Data
Essential Component
Information Loss
Subsequence
Mining
Interpolate
Interpolation
Series
Prediction

Keywords

  • Adaptive sliding window
  • Change points
  • Segmentation
  • Sliding window
  • Time series

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Adaptive sliding window algorithm for weather data segmentation. / BenYahmed, Yahyia; Abu Bakar, Azuraliza; Hamdan, Abdul Razak; Ahmed, Almahdi; Syed Abdullah, Sharifah Mastura.

In: Journal of Theoretical and Applied Information Technology, Vol. 80, No. 2, 20.10.2015, p. 322-333.

Research output: Contribution to journalArticle

@article{501e9516178140419eafcf06740593fe,
title = "Adaptive sliding window algorithm for weather data segmentation",
abstract = "Data segmentation is one of the primary tasks of time series mining. This task is often used to generate interesting subsequences from a large time series sequence. Segmentation is one of the essential components in extracting significant patterns of weather time series data, which may be useful in identifying the trend and changes in weather prediction. The task use interpolation to approximate the signal with a best-fitting series and return the last point of the segments as change point or as a sequence of time points as a window. Sliding window algorithm (SWA) is a well-known time series data segmentation method, in which a segment with an error threshold and fixed window size is created when the change point is reached. In actual data such as weather data, SWA is unsuitable because appropriate error threshold and change point are required to avoid information loss. In this paper, we propose an adaptive sliding window algorithm (ASWA) that categorizes weather time series data based on the change point information.",
keywords = "Adaptive sliding window, Change points, Segmentation, Sliding window, Time series",
author = "Yahyia BenYahmed and {Abu Bakar}, Azuraliza and Hamdan, {Abdul Razak} and Almahdi Ahmed and {Syed Abdullah}, {Sharifah Mastura}",
year = "2015",
month = "10",
day = "20",
language = "English",
volume = "80",
pages = "322--333",
journal = "Journal of Theoretical and Applied Information Technology",
issn = "1992-8645",
publisher = "Asian Research Publishing Network (ARPN)",
number = "2",

}

TY - JOUR

T1 - Adaptive sliding window algorithm for weather data segmentation

AU - BenYahmed, Yahyia

AU - Abu Bakar, Azuraliza

AU - Hamdan, Abdul Razak

AU - Ahmed, Almahdi

AU - Syed Abdullah, Sharifah Mastura

PY - 2015/10/20

Y1 - 2015/10/20

N2 - Data segmentation is one of the primary tasks of time series mining. This task is often used to generate interesting subsequences from a large time series sequence. Segmentation is one of the essential components in extracting significant patterns of weather time series data, which may be useful in identifying the trend and changes in weather prediction. The task use interpolation to approximate the signal with a best-fitting series and return the last point of the segments as change point or as a sequence of time points as a window. Sliding window algorithm (SWA) is a well-known time series data segmentation method, in which a segment with an error threshold and fixed window size is created when the change point is reached. In actual data such as weather data, SWA is unsuitable because appropriate error threshold and change point are required to avoid information loss. In this paper, we propose an adaptive sliding window algorithm (ASWA) that categorizes weather time series data based on the change point information.

AB - Data segmentation is one of the primary tasks of time series mining. This task is often used to generate interesting subsequences from a large time series sequence. Segmentation is one of the essential components in extracting significant patterns of weather time series data, which may be useful in identifying the trend and changes in weather prediction. The task use interpolation to approximate the signal with a best-fitting series and return the last point of the segments as change point or as a sequence of time points as a window. Sliding window algorithm (SWA) is a well-known time series data segmentation method, in which a segment with an error threshold and fixed window size is created when the change point is reached. In actual data such as weather data, SWA is unsuitable because appropriate error threshold and change point are required to avoid information loss. In this paper, we propose an adaptive sliding window algorithm (ASWA) that categorizes weather time series data based on the change point information.

KW - Adaptive sliding window

KW - Change points

KW - Segmentation

KW - Sliding window

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=84944539845&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944539845&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84944539845

VL - 80

SP - 322

EP - 333

JO - Journal of Theoretical and Applied Information Technology

JF - Journal of Theoretical and Applied Information Technology

SN - 1992-8645

IS - 2

ER -