SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

نویسنده

  • Alessandro Flammini
چکیده

searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggesstions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA, 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any oenalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An improvement of symbolic aggregate approximation distance measure for time series

Symbolic Aggregate approXimation (SAX) as a major symbolic representation has been widely used in many time series data mining applications. However, because a symbol is mapped from the average value of a segment, the SAX ignores important information in a segment, namely the trend of the value change in the segment. Such a miss may cause a wrong classification in some cases, since the SAX repr...

متن کامل

A Weighted Minimum Distance Using Hybridization of Particle Swarm Optimization and Bacterial Foraging

In a previous work we used a popular bio-inspired algorithm; particle swam optimization (PSO) to improve the performance of a well-known representation method of time series data which is the symbolic aggregate approximation (SAX), where PSO was used to propose a new weighted minimum distance WMD for SAX to recover some of the information loss resulting from the original minimum distance MINDIS...

متن کامل

1d-SAX: A Novel Symbolic Representation for Time Series

SAX (Symbolic Aggregate approXimation) is one of the main symbolization technique for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that contain each an information about the average and the trend of the series on a segment. We compare the efficie...

متن کامل

Towards a Faster Symbolic Aggregate Approximation Method

The similarity search problem is one of the main problems in time series data mining. Traditionally, this problem was tackled by sequentially comparing the given query against all the time series in the database, and returning all the time series that are within a predetermined threshold of that query. But the large size and the high dimensionality of time series databases that are in use nowad...

متن کامل

Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation

Bio-inspired optimization algorithms have been gaining more popularity recently. One of the most important of these algorithms is particle swarm optimization (PSO). PSO is based on the collective intelligence of a swam of particles. Each particle explores a part of the search space looking for the optimal position and adjusts its position according to two factors; the first is its own experienc...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013