scholarly journals Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

2014 ◽  
Vol 6 (12) ◽  
pp. 12639-12665 ◽  
Author(s):  
Benoit Parmentier
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Trend Analysis ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Seasonal Trend

scholarly journals Moving dynamic principal component analysis for non-stationary multivariate time series

2021 ◽  
Author(s):  
Fayed Alshammri ◽  
Jiazhu Pan
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Series Data ◽  
Dynamic Principal Component Analysis ◽  
Dynamic Relationships

AbstractThis paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number of final retained components is proposed. An advance correlation matrix is developed to evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method are given. Many simulation experiments show our approach performs well on both stationary and non-stationary data. Real data examples are also presented as illustrations. We develop four packages using the statistical software R that contain the needed functions to obtain and assess the results of the proposed method.

scholarly journals Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

2021 ◽  
Vol 13 (20) ◽  
pp. 4123
Author(s):  
Hanqi Wang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Fengyu Xu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Evaluation Indexes ◽  
Learning Network ◽  
Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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