A Time Series Mining Approach for Agricultural Area Detection

2020 ◽  
Vol 6 (3) ◽  
pp. 537-546
Author(s):  
Joao Paulo Da Silva ◽  
Jurandir Zullo ◽  
Luciana Alvim Santos Romani
Keyword(s):  
Time Series ◽  
Agricultural Area ◽  
Time Series Mining

An approach based on satellite image time series mining to identify region susceptible to desertification

2014 ◽  
Author(s):  
R. Scrivani ◽  
R. R. V. Goncalves ◽  
L. A. S. Romani ◽  
S. R. M. Oliveira ◽  
E. D. Assad
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Time Series Mining

Time Series Mining

2008 ◽  
pp. 14-43
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Dimension Reduction ◽  
Similarity Measures ◽  
Sequential Patterns ◽  
Sequence Mining ◽  
Periodic Patterns ◽  
Background Material ◽  
Time Series Mining ◽  
Data Discretization

In this chapter, we will first give the background and review existing works in time series mining. The background material will include commonly used similarity measures and techniques for dimension reduction and data discretization. Then we will examine techniques to discover periodic and sequential patterns. This will lay the groundwork for the subsequent three chapters on mining dense periodic patterns, incremental sequence mining, and mining progressive patterns.

Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

2019 ◽  
Author(s):  
Zachary Zimmerman ◽  
Nader Shakibay Senobari ◽  
Gareth Funning ◽  
Evangelos Papalexakis ◽  
Samet Oymak ◽  
...  
Keyword(s):  
Time Series ◽  
Fast Moving ◽  
The Face ◽  
Time Series Mining

scholarly journals Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

2010 ◽  
Vol 114 (11) ◽  
pp. 2500-2512 ◽  
Author(s):  
Olivier Merlin ◽  
Benoit Duchemin ◽  
Olivier Hagolle ◽  
Frédéric Jacob ◽  
Benoit Coudert ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Agricultural Area

A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

2013 ◽  
Vol 51 (1) ◽  
pp. 140-150 ◽  
Author(s):  
Luciana Alvim S. Romani ◽  
Ana Maria H. de Avila ◽  
Daniel Y. T. Chino ◽  
Jurandir Zullo ◽  
Richard Chbeir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Remote Sensing Imagery ◽  
Time Series Mining ◽  
Multitemporal Remote Sensing ◽  
New Time

scholarly journals Time Series Classification with Discrete Wavelet Transformed Data

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1361-1377 ◽  
Author(s):  
Daoyuan Li ◽  
Tegawende F. Bissyande ◽  
Jacques Klein ◽  
Yves Le Traon
Keyword(s):  
Time Series ◽  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Time Series Data ◽  
Knowledge Engineering ◽  
Series Data ◽  
Discrete Wavelet ◽  
Time Series Classification ◽  
Time Series Mining ◽  
Compressed Data

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

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