Time Series Classification Method Based on Longest Common Subsequence and Textual Approximation

2012 ◽  
Author(s):  
Abdulla-Al-Maruf ◽  
Hung-Hsuan Huang ◽  
Kyoji Kawagoe
Keyword(s):  
Time Series ◽  
Longest Common Subsequence ◽  
Classification Method ◽  
Time Series Classification ◽  
Common Subsequence

scholarly journals Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3733
Author(s):  
Wei Liu ◽  
Jian Wang ◽  
Jiancheng Luo ◽  
Zhifeng Wu ◽  
Jingdong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Time Profile ◽  
Classification Method ◽  
Guizhou Province ◽  
Optical Data ◽  
Time Series Classification ◽  
Mountainous Areas ◽  
Mountain Areas ◽  
Actual Distribution ◽  
Sar Data

Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland.

scholarly journals A Metric Learning-Based Univariate Time Series Classification Method

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 288
Author(s):  
Kuiyong Song ◽  
Nianbin Wang ◽  
Hongbin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Classification Method ◽  
Series Data ◽  
Classification Error ◽  
Time Series Classification ◽  
Classification Error Rate ◽  
Univariate Time Series

High-dimensional time series classification is a serious problem. A similarity measure based on distance is one of the methods for time series classification. This paper proposes a metric learning-based univariate time series classification method (ML-UTSC), which uses a Mahalanobis matrix on metric learning to calculate the local distance between multivariate time series and combines Dynamic Time Warping(DTW) and the nearest neighbor classification to achieve the final classification. In this method, the features of the univariate time series are presented as multivariate time series data with a mean value, variance, and slope. Next, a three-dimensional Mahalanobis matrix is obtained based on metric learning in the data. The time series is divided into segments of equal intervals to enable the Mahalanobis matrix to more accurately describe the features of the time series data. Compared with the most effective measurement method, the related experimental results show that our proposed algorithm has a lower classification error rate in most of the test datasets.

scholarly journals A T-CNN Time Series Classification Method

2021 ◽  
Author(s):  
Junlu Wang ◽  
Su Li ◽  
Wanting Ji ◽  
Tian Jiang ◽  
Baoyan Song
Keyword(s):  
Time Series ◽  
Classification Accuracy ◽  
Transformation Method ◽  
Streaming Data ◽  
Classification Method ◽  
Gram Matrix ◽  
Event Analysis ◽  
Time Series Classification ◽  
Basic Task ◽  
Low Efficiency

Abstract Time series classification is a basic task in the field of streaming data event analysis and data mining. The existing time series classification methods have the problems of low classification accuracy and low efficiency. To solve these problems, this paper proposes a T-CNN time series classification method based on a Gram matrix. Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved CNN time series classification method, which introduces the Toeplitz convolution kernel matrix into convolution layer calculation. Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental results show that, compared with the existing methods, our T-CNN time series classification method has great advantages in efficiency and accuracy.

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