On similarity queries for time-series data: Constraint specification and implementation

“…However, time series data in continuous domains is inherently inexact, due to the unavoidable imprecision of measuring devices and clocking strategies. This forces us to work with the approximate version of the various matching problems [8]: Given a tolerance …”

Section: The Suggested Algorithmmentioning

confidence: 99%

An Information Retrieval Approach to Predicting Meteorological Data

Kidron∗¹,

Klein∗²

2007

International Journal of Modelling and Simulation

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An Information Retrieval approach to the prediction of values of time series is suggested. Instead of trying to model the data, a pattern is searched for in a history file serving as "text", and relevant occurrences are used as basis for extrapolation. The application is to meteorological data, providing short-term predictions for certain weather features. The experiments show that the algorithm has a good ability of predicting the requested information, and that the predictions are far more accurate than those provided by an analytical prediction procedure, based on approximations using Fourier transforms.

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“…Their present support for spatial time series is at best rudimentary. General time series support is an active field of research, however [3,7,8,11,13].…”

Section: Gis'02mentioning

confidence: 99%

“…The simplest approach to define similarity between two sequences is viewing them as vector and using Euclidean distance as similarity measure [2,7,9,12,13,15,16,24,25]. Such techniques cannot be easily applied to sequences having different length or sampling rate.…”

Section: Gis'02mentioning

confidence: 99%

Aggregation and comparison of trajectories

Meratnia¹,

By²

2002

Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems - GIS '02

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Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This is where trajectory determination plays an important role, and on which classification, aggregation and comparison methods must be built. The purpose of aggregation is to identify similar trajectories and to represent them by a single trajectory.Although much work has been done in similarity measurements for time series data, they mainly deal with one dimensional time series. On the other hand, they are good for short time series and in absence of noise, which is definitely not the case for moving objects. This paper describes different approaches to aggregate similar trajectories.

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On similarity queries for time-series data: Constraint specification and implementation

Cited by 211 publications

References 13 publications

Selective pattern matching method for time-series forecasting

Selective pattern matching method for time-series forecasting

An Information Retrieval Approach to Predicting Meteorological Data

Aggregation and comparison of trajectories

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