Performance evaluation of a two-stage clustering technique for time-series data

Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

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Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Complexity ◽

10.1155/2019/2782715 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Muhammad Faisal ◽

Alaa Mohamd Shoukry ◽

Showkat Gani ◽

...

Keyword(s):

Time Series ◽

Hybrid Model ◽

Prediction Accuracy ◽

Optimal Allocation ◽

Time Series Data ◽

Hybrid Models ◽

Indus Basin ◽

Series Data ◽

Stage Model ◽

Two Stage

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

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A two-stage model for multiple time series data of counts

Biostatistics ◽

10.1093/biostatistics/3.1.21 ◽

2002 ◽

Vol 3 (1) ◽

pp. 21-32 ◽

Cited By ~ 9

Author(s):

K. Berhane

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Multiple Time ◽

Stage Model ◽

Multiple Time Series ◽

Two Stage

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Development of Hybrid Methods for Prediction of Principal Mineral Resources

Mathematical Problems in Engineering ◽

10.1155/2021/6362660 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Maria Qurban ◽

Xiang Zhang ◽

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Muhammad Faisal ◽

...

Keyword(s):

Mineral Resource ◽

Time Series ◽

Time Series Data ◽

Hybrid Methods ◽

Mineral Resources ◽

Hybrid Models ◽

Series Data ◽

Two Stage ◽

One Stage ◽

Decomposition Strategies

Accurate estimation of the mining process is vital for the optimal allocation of mineral resources. The development of any country is precisely connected with the management of mineral resources. Therefore, the forecasting of mineral resources contributed much to management, planning, and a maximum allocation of mineral resources. However, it is challenging because of its multiscale variability, nonlinearity, nonstationarity, and high irregularity. In this paper, we proposed two revised hybrid methods to address these issues to predict mineral resources. Our methods are based on denoising, decomposition, prediction, and ensemble principles that are applied to the production of mineral resource time-series data. The performance of the proposed methods is compared with the existing traditional one-stage model (without denoised and decomposition strategies) and two-stage hybrid models (based on denoised strategy), and three-stage hybrid models (with denoised and decomposition strategies). The performance of these methods is evaluated using mean relative error (MRE), mean absolute error (MAE), and mean square error (MSE) as evaluation measures for the production of four principle mineral resources of Pakistan. It is concluded that the proposed framework for the prediction of mineral resources indicated better performance as compared to other existing one-stage, two-stage, and three-stage models. Furthermore, the prediction accuracy of the revised hybrid model is improved by reducing the complexity of the production of mineral resource time-series data.

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Selection of Input Factors and Comparison of Machine Learning Models for Prediction of Dissolved Oxygen in Gyeongan Stream

Journal of Korean Society of Environmental Engineers ◽

10.4491/ksee.2021.43.3.206 ◽

2021 ◽

Vol 43 (3) ◽

pp. 206-217

Author(s):

Min Ji Kim ◽

Seon Jeong Byeon ◽

Kyung Min Kim ◽

Johng-Hwa Ahn

Keyword(s):

Neural Network ◽

Machine Learning ◽

Electrical Conductivity ◽

Time Series ◽

Performance Evaluation ◽

Suspended Solids ◽

Time Series Data ◽

Series Data ◽

Learning Models ◽

Machine Learning Models

Objectives : In this study, we select input factors for machine learning models to predict dissolved oxygen (DO) in Gyeongan Stream and compare results of performance evaluation indicators to find the optimal model.Methods : The water quality data from the specific points of Gyeongan Stream were collected between January 15, 1998 and December 30, 2019. The pretreatment data were divided into train and test data with the ratio of 7:3. We used random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU) among machine learning. RF and ANN were tested by both random split and time series data, while CNN and GRU conducted the experiment using only time series data. Performance evaluation indicators such as square of the correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to compare the optimal results for the models.Results and Discussion : Based on the RF variable importance results and references, water temperature, pH, electrical conductivity, PO4-P, NH4-N, total phosphorus, suspended solids, and NO3-N were used as input factors. Both RF and ANN performed better with time series data than random split. The model performance was good in order of RF > CNN > GRU > ANN.Conclusions : The eight input factors (water temperature, pH, electrical conductivity, PO4-P, NH4-N, total phosphorus, suspended solids, and NO3-N) were selected for machine learning models to predict DO in Gyeongan Stream. The best model for DO prediction was the RF model with time series data. Therefore, we suggest that the RF with the eight input factors could be used to predict the DO in streams.

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CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data

Bioinformatics ◽

10.1093/bioinformatics/btv532 ◽

2015 ◽

Vol 32 (3) ◽

pp. 388-397 ◽

Cited By ~ 5

Author(s):

Işık Barış Fidaner ◽

Ayca Cankorur-Cetinkaya ◽

Duygu Dikicioglu ◽

Betul Kirdar ◽

Ali Taylan Cemgil ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Two Stage

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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