On analyzing GNSS displacement field variability of Taiwan: Hierarchical Agglomerative Clustering based on Dynamic Time Warping technique

Kumar, Utpal; Legendre, C. P.; Lee, Jian‐Cheng; Zhao, Li; Chao, Benjamin F.

doi:10.1016/j.cageo.2022.105243

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…Dynamic Time Warping (DTW) merupakan algoritma perhitungan jarak yang berhasil diterapkan untuk mengatasi kecepatan waktu yang berbeda pada dua data deret waktu [12]. Senin [17] menjelaskan pada tahap awal proses penghitungan DTW akan dibentuk matriks jarak yang kemudian disebut dengan matriks biaya lokal berukuran untuk dua data deret waktu X dan Y. Jika dituliskan dalam bentuk vektor, maka…”

Section: Jarak Dynamic Time Warpingunclassified

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

Anugrahayu,

Azmi

2023

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The tendency of investors to choose investments with maximum return and minimal risk causes the need for diversification in a portfolio to form an optimal portfolio. A lot of research on stock portfolio optimization has been conducted extensively, but not many have tried to apply machine learning concepts such as clustering analysis to accelerate the establishment of a model that can have a positive effect on the time and cost efficiency of portfolio management. However, clustering is only limited to determining the optimal stock candidate, so it is necessary to add another optimization model to calculate the portfolio weight. Based on these problems, this study carried out portfolio optimization using Mean-Variance (MV) and Mean Absolute Deviation (MAD) model based on K-Medoids Clustering by Dynamic Time Warping approach using Monte Carlo-Expected Tail Loss for risk analysis. Based on the analysis results, the MAD portfolio is more optimal than the MV portfolio by the MAD portfolio consists of five stocks, namely BMRI shares with a weight of 0.06243, UNTR shares of 0.08658, BBRI shares of 0.10285, BBCA of 0.53623, and KLBF shares of 0.21191 are the best optimal portfolios. The optimal portfolio of the MAD model has a rate of return of 87.836% in May 2017 - December 2022 with a portfolio performance of 0.03704, while the resulting risk level based on Carlo-Expected Tail Loss is 2.2416%.

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Section: Jarak Dynamic Time Warpingunclassified

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

Anugrahayu,

Azmi

2023

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“…Hierarchical clustering can effectively improve the accuracy and computational efficiency of clustering by distinguishing outlier points and determining the optimal number of clusters. Utpal Kumar et al [16] use hierarchical agglomerative clustering and the dynamic time warping to cluster 11 years' worth of 3-D displacement data. Lerato and Niesler [17] have proposed a multi-stage agglomerative hierarchical clustering approach aimed at large datasets of speech segments.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Principal Component Analysis and Hierarchical Clustering to Develop Driving Cycles: A Case Study in Zhenjiang

et al. 2023

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Accurate driving cycles are key for effectively evaluating electric vehicle performance. The K-means algorithm is widely used to construct driving cycles; however, this algorithm is sensitive to outliers, and determining the K value is difficult. In this paper, a novel driving cycle construction method based on principal component analysis and hierarchical clustering is proposed. Real road vehicle data were collected, denoised, and divided into vehicle microtrip data. The eigenvalues of the microtrips were extracted, and their dimensions were reduced through principal component analysis. Hierarchical clustering was then performed to classify the microtrips, and a representative set of microtrips was randomly selected to construct the driving cycle. The constructed driving cycle was verified and compared with a driving cycle constructed using K-means clustering and the New European Driving Cycle. The average relative eigenvalue error, maximum speed acceleration probability distribution difference rate, average cycle error, and simulated relative power consumption error per 100 km between the hierarchical driving cycle and the real road data were superior to those of the K-means driving cycle, which indicated the effectiveness of the proposed method. Though the methodology proposed in this paper has not been verified in other regions, it provided a certain reference value for other research of the developing driving cycle.

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DASA: An Efficient Data Aggregation Algorithm for LoRa Enabled Fog Layer in Smart Agriculture

Vyas

Anand

Yadav

et al. 2023

Lecture Notes in Networks and Systems

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On analyzing GNSS displacement field variability of Taiwan: Hierarchical Agglomerative Clustering based on Dynamic Time Warping technique

Cited by 7 publications

References 45 publications

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

Utilizing Principal Component Analysis and Hierarchical Clustering to Develop Driving Cycles: A Case Study in Zhenjiang

DASA: An Efficient Data Aggregation Algorithm for LoRa Enabled Fog Layer in Smart Agriculture

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