Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

Download Full-text

Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

10.5194/hess-2020-541 ◽

2020 ◽

Author(s):

Kuk-Hyun Ahn

Keyword(s):

Missing Values ◽

The Other ◽

Dependence Structure ◽

Proposed Model ◽

Pee Dee ◽

Streamflow Estimation ◽

Vine Copula ◽

Bivariate Copula ◽

Pee Dee River ◽

Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

Download Full-text

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2017100103 ◽

2017 ◽

Vol 8 (4) ◽

pp. 30-53 ◽

Cited By ~ 3

Author(s):

Warut Pannakkong ◽

Van-Hai Pham ◽

Van-Nam Huynh

Keyword(s):

Hybrid Model ◽

Forecast Error ◽

Moving Average ◽

Data Sets ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Hybrid Forecasting ◽

Us Dollar ◽

Different Levels ◽

Prediction Capability

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

Download Full-text

Forecasting Time Series Movement Direction with Hybrid Methodology

Journal of Probability and Statistics ◽

10.1155/2017/3174305 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Salwa Waeto ◽

Khanchit Chuarkham ◽

Arthit Intarasit

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Hybrid Model ◽

Moving Average ◽

Forecast Accuracy ◽

Movement Direction ◽

Support Vector ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Hybrid Methodology

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.

Download Full-text

An algorithm for detecting intense anomalous changes in the time dependence of ionospheric parameters

Computer Optics ◽

10.18287/2412-6179-2019-43-6-1064-1071 ◽

2019 ◽

Vol 43 (6) ◽

pp. 1064-1071 ◽

Cited By ~ 5

Author(s):

N.V. Fetisova

Keyword(s):

Weather Forecasting ◽

Moving Average ◽

Space Weather Forecasting ◽

Multi Scale ◽

Combined Application ◽

Proposed Model ◽

Moving Average Models ◽

Critical Frequency Fof2 ◽

On Line ◽

Model Components

The paper presents a modified multicomponent model of ionospheric parameter time series. The model describes regular variations and anomalous changes of a multi-scale structure that characterize the occurrence of ionospheric irregularities. Identification of the model components is based on a combined application of the wavelet transform and autoregressive-integrated moving average models. An algorithm for analyzing ionospheric parameters has been developed on the basis of the proposed model. The algorithm allows the intensive ionospheric anomalies characterizing the occurrence of strong ionospheric storms to be detected on-line. Results of the evaluation of the algorithm performance are presented. The evaluation is performed by the example of processing and analyzing hourly and 15-minute data on the ionospheric critical frequency (foF2) during magnetic storms in 2015 – 2017. The performed estimations showed the efficiency of the algorithm and the possibility of its application for space weather forecasting.

Download Full-text

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

Scientific Reports ◽

10.1038/s41598-021-00948-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yongbin Wang ◽

Chunjie Xu ◽

Sanqiao Yao ◽

Lei Wang ◽

Yingzheng Zhao ◽

...

Keyword(s):

South Africa ◽

Empirical Mode Decomposition ◽

Hybrid Model ◽

Moving Average ◽

Ensemble Empirical Mode Decomposition ◽

Data Driven ◽

Mortality Data ◽

Hybrid Technique ◽

Mode Decomposition ◽

The Individual

AbstractIn this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.

Download Full-text

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

10.4018/978-1-6684-2408-7.ch076 ◽

2022 ◽

pp. 1532-1558

Author(s):

Warut Pannakkong ◽

Van-Hai Pham ◽

Van-Nam Huynh

Keyword(s):

Hybrid Model ◽

Forecast Error ◽

Moving Average ◽

Data Sets ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Hybrid Forecasting ◽

Us Dollar ◽

Different Levels ◽

Prediction Capability

Download Full-text

Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach

Energy Economics ◽

10.1016/j.eneco.2020.104774 ◽

2020 ◽

Vol 88 ◽

pp. 104774

Author(s):

Xingyu Dai ◽

Qunwei Wang ◽

Donglan Zha ◽

Dequn Zhou

Keyword(s):

Exchange Rate ◽

Scale Dependence ◽

Dependence Structure ◽

Multi Scale ◽

Copula Approach ◽

Vine Copula

Download Full-text

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189785 ◽

2021 ◽

pp. 1-17

Author(s):

Nuzhat Fatema ◽

H Malik ◽

Mutia Sobihah Binti Abd Halim

Keyword(s):

Monte Carlo ◽

Hybrid Model ◽

Medical Tourism ◽

Moving Average ◽

Hybrid Approach ◽

Mode Decomposition ◽

Hybrid Forecasting ◽

Input Variables ◽

Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

Download Full-text

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

Journal of Econometric Methods ◽

10.1515/jem-2020-0004 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Hiroyuki Kawakatsu

Keyword(s):

Moving Average ◽

Curse Of Dimensionality ◽

Moving Averages ◽

Conditional Covariance ◽

Arch Models ◽

Empirical Application ◽

Proposed Model ◽

Out Of Sample ◽

Weight Model ◽

Weighted Moving Average

AbstractThis paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

Download Full-text

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs13112166 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2166

Author(s):

Xin Yang ◽

Rui Liu ◽

Mei Yang ◽

Jingjue Chen ◽

Tianqiang Liu ◽

...

Keyword(s):

Land Use ◽

Hybrid Model ◽

Landslide Susceptibility ◽

Spatial Information ◽

Landslide Susceptibility Mapping ◽

Ratio Method ◽

Support Vector ◽

Susceptibility Map ◽

Conditioning Factors ◽

Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

Download Full-text