TY - CHAP
T1 - A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series
AU - Cardell-Oliver, Rachel
PY - 2016
Y1 - 2016
N2 - Conserving water is a critical problem and characterising how households in communities use water is a first step for reducing consumption. This paper introduces a method for discovering habits in smart water meter time series. Habits are household activities that recur in a predictable way, such as watering the garden at 6 am twice a week. Discovering habit patterns automatically is a challenging data mining task. Habit patterns are not only periodic, nor only seasonal, and they may not be frequent. Their recurrences are partial periodic patterns with a very large number of candidates. Further, the recurrences in real data are imperfect, making accurate matching of observations with proposed patterns difficult. The main contribution of this paper is an efficient, robust and accurate Habit Detection Algorithm (HDA) for discovering regular activities in smart meter time series with evaluation the performance of the algorithm and its ability to discover valuable insights from real-world data sets.
AB - Conserving water is a critical problem and characterising how households in communities use water is a first step for reducing consumption. This paper introduces a method for discovering habits in smart water meter time series. Habits are household activities that recur in a predictable way, such as watering the garden at 6 am twice a week. Discovering habit patterns automatically is a challenging data mining task. Habit patterns are not only periodic, nor only seasonal, and they may not be frequent. Their recurrences are partial periodic patterns with a very large number of candidates. Further, the recurrences in real data are imperfect, making accurate matching of observations with proposed patterns difficult. The main contribution of this paper is an efficient, robust and accurate Habit Detection Algorithm (HDA) for discovering regular activities in smart meter time series with evaluation the performance of the algorithm and its ability to discover valuable insights from real-world data sets.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84955253116&origin=resultslist&sort=plf-f&src=s&st1=A+Habit+Detection+Algorithm+for+Discovering+Recurrent+Patterns+in+Smart+Meter+Time+Series&st2=&sid=7AF89F655958D18B3D315A9D4106EC48.wsnAw8kcdt7IPYLO0V48gA%3a2490&sot=b&sdt=b&sl=104&s=TITLE-ABS-KEY%28A+Habit+Detection+Algorithm+for+Discovering+Recurrent+Patterns+in+Smart+Meter+Time+Series%29&relpos=0&citeCnt=0&searchTerm=
U2 - 10.1007/978-3-319-29009-6_6
DO - 10.1007/978-3-319-29009-6_6
M3 - Chapter
SN - 9783319290089
VL - 9546
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 127
BT - Big Data Analytics in the Social and Ubiquitous Context
A2 - Janssen, Frederik
A2 - Chin, Alvin
A2 - Atzmueller, Martin
A2 - Trattner, Christoph
A2 - Schweizer, Immanuel
PB - Springer
T2 - 5th International Workshop on Mining Ubiquitous and Social Environments
Y2 - 15 September 2014 through 15 September 2014
ER -
