scholarly journals Development of Hybrid Methods for Prediction of Principal Mineral Resources

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.

scholarly journals Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
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.

scholarly journals IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2615
Author(s):  
Xinyao Sun ◽  
Aaron Zimmer ◽  
Subhayan Mukherjee ◽  
Parwant Ghuman ◽  
Irene Cheng
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Optimization Problems ◽  
Ground Deformation ◽  
Series Data ◽  
Two Stage ◽  
Real Time Processing ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Model

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.

scholarly journals Analyzing and Forecasting HIV Data Using Hybrid Time Series Models

2019 ◽  
pp. 1-12
Author(s):  
Liming Xie
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Hybrid Methods ◽  
Moving Average ◽  
Series Data ◽  
People Living With Hiv ◽  
The World ◽  
Linear And Nonlinear ◽  
Original Time ◽  
And Control

In real work, we often confront complete linear and nonlinear time series data. But some time series are not pure linear and nonlinear, or complicated one, we need apply two or more models to analyze and predict them. It is necessary to explore and find some novel time series hybrid methods to solve it. Human Immunodeficiency and Virus (HIV) is one of intractable and trouble diseases in the world. Thus, the author of this article wants to analyze and probe into some novel time series methods to get breaking breach in the epidemiology that find some rules in the incidence, distribution, pathogen, and control of HIV in a population.  In this article, to find the best model, auto.arima function is applied to the original time series data to determine autoregressive integrated moving average, ARIMA(0,0,0); ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH), that is, ARIMA-GARCH (1,1) model is used to analyze numbers of people living with HIV for the data of HIV in the world such some important parameters as mu, ar1, ar2, omega, alpha 1, or beta 1 and some specific tests, for example, Jarque-Bera Test, Shapiro-Wilk Test, Ljung-Box Test, etc. Using ARIMA (0, 0, 0) and SARIMA (0,2), seasonal ARIMA, to predict the future values and trends after 2015. Both suggest identical results.

scholarly journals Time Series Analysis of Indian Spices Export and Prices

2019 ◽  
Author(s):  
S. Anusha ◽  
B. Srinivasa Kumar ◽  
D. Satish Kumar
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Time Series Data ◽  
Secondary Data ◽  
Network Models ◽  
Policy Formulation ◽  
Hybrid Models ◽  
Series Data ◽  
Agricultural Commodity ◽  
Agricultural Exports

India is the land of spices and is the largest producer, consumer and the exporter of spices in the world. Spices are an important component of Indian Agricultural Exports earning valuable foreign exchange and are the source of livelihood for millions of small and marginal farmers across different states of the country. Modeling of agricultural exports in general and spices exports in particular is important in the contest of spices exports being a priority area for Indian policy makers. Time series modeling of agricultural commodity exports is an active area of research in recent times. Generally Box Jenkins approach (ARIMA) is the referred technique for this purpose. When data exhibits volatility clustering, ARCH/GARCH models are used .When the data does not support linearity assumptions neutral network models are used. However, real world time series data is believed to be a combination of linear and non-linear patterns. In this context, Hybrid models which are a combination of AR models and Artificial Neural Networks are providing more accurate forecasts. The present study, using secondary data for the period from 1960-61 to 2017-18 applies three hybrid models for forecasting Indian spices exports both in terms of volume and prices. Based on the RMSE each model is evaluated and finally model with least RMSE was selected for forecasting both volume and unit prices of total spices export for the coming 10 years (2018-19 to 2027-28). Analysis of data was done with the help of open source software R. Results from the study show that, Hybrid model consisting of ARIMA, Exponential Smoothing and Tbats Model with unequal weights was found to be the best model on the basis of RMSE for forecasting Indian spices exports. Thus, for both forecasting and policy formulation the hybrid model is recommended.

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

2015 ◽  
Vol 32 (3) ◽  
pp. 388-397 ◽  
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

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

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

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

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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