A Novel Method for Assessing Event Impacts on Event-Driven Time Series

United event driven figurings have provoked various regular advances, including web programs [1] and e-business. Given the present status of Bayesian speculation, electrical originators regularly need the portrayal of associated records, which embodies the bewildering principles of AI. Want, our new response for the examination of over the top composition PC programs, is the response for these issues. This takes after from the impression of Byzantine adjustment to interior disappointment

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A Novel Method Based on Data Visual Autoencoding for Time-Series Classification

Proceedings of the 2015 Chinese Intelligent Automation Conference - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-662-46469-4_10 ◽

2015 ◽

pp. 97-104 ◽

Cited By ~ 1

Author(s):

Chen Qian ◽

Yan Wang ◽

Lei Guo

Keyword(s):

Time Series ◽

Time Series Classification ◽

Novel Method

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A Novel Method of AOT Evaluation for Change-Point Detection on Synthetic Time-series

2018 Chinese Automation Congress (CAC) ◽

10.1109/cac.2018.8623760 ◽

2018 ◽

Author(s):

Jin-Peng Qi ◽

Ying Zhu ◽

Fang Pu

Keyword(s):

Time Series ◽

Change Point ◽

Change Point Detection ◽

Novel Method ◽

Synthetic Time Series ◽

Point Detection

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A Novel Method for Separating Woody and Herbaceous Time Series

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.85.7.509 ◽

2019 ◽

Vol 85 (7) ◽

pp. 509-520 ◽

Cited By ~ 1

Author(s):

Qiang Zhou ◽

Shuguang Liu ◽

Michael J Hill

Keyword(s):

Time Series ◽

Novel Method

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Targets Detection in Sea Clutter Based on Echo State Network

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.4.255 ◽

2012 ◽

Vol 4 ◽

pp. 255-258

Author(s):

Zhan Xu ◽

Jian Wei Wan ◽

Gang Li ◽

Fang Su

Keyword(s):

Time Series ◽

Mean Squared Error ◽

Real Life ◽

Radar Data ◽

Recursive Least Squares ◽

Echo State Network ◽

Sea Clutter ◽

Rls Algorithm ◽

Real Life Data ◽

Novel Method

A novel method to predict the sea clutter time series and detect target embedded in sea clutter is presented. The method is actually a recurrent neural network called an echo state network (ESN). A recursive least squares (RLS) algorithm is used for updating the output weights of ESN. A set of time series from IPIX radar data is tested. Numerical experiments reveal that the proposed network shows higher prediction precision in pure sea clutter data. Moreover, the mean squared error (MSE) between real-life data and prediction value by ESN can be used to detect target effectively.

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