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

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.

scholarly journals A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Eui-Jin Kim ◽  
Ho-Chul Park ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim
Keyword(s):  
Correlation Analysis ◽  
Hybrid Model ◽  
Travel Demand ◽  
Hybrid Approach ◽  
Road Networks ◽  
Travel Speed ◽  
Variational Mode Decomposition ◽  
Urban Road ◽  
Urban Travel ◽  
Mode Decomposition

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.

scholarly journals Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1722
Author(s):  
Yu-Sheng Kao ◽  
Kazumitsu Nawata ◽  
Chi-Yo Huang
Keyword(s):  
Energy Consumption ◽  
Empirical Study ◽  
Moving Average ◽  
Primary Energy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Primary Energy Consumption ◽  
Mode Decomposition ◽  
Study Results ◽  
Hybrid Forecasting

Forecasting energy consumption is not easy because of the nonlinear nature of the time series for energy consumptions, which cannot be accurately predicted by traditional forecasting methods. Therefore, a novel hybrid forecasting framework based on the ensemble empirical mode decomposition (EEMD) approach and a combination of individual forecasting models is proposed. The hybrid models include the autoregressive integrated moving average (ARIMA), the support vector regression (SVR), and the genetic algorithm (GA). The integrated framework, the so-called EEMD-ARIMA-GA-SVR, will be used to predict the primary energy consumption of an economy. An empirical study case based on the Taiwanese consumption of energy will be used to verify the feasibility of the proposed forecast framework. According to the empirical study results, the proposed hybrid framework is feasible. Compared with prediction results derived from other forecasting mechanisms, the proposed framework demonstrates better precisions, but such a hybrid system can also be seen as a basis for energy management and policy definition.

scholarly journals River flow time series using least squares support vector machines

2011 ◽  
Vol 15 (6) ◽  
pp. 1835-1852 ◽  
Author(s):  
R. Samsudin ◽  
P. Saad ◽  
A. Shabri
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Hybrid Model ◽  
River Flow ◽  
Moving Average ◽  
Peninsular Malaysia ◽  
Support Vector ◽  
Group Method ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

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

2017 ◽  
Vol 8 (4) ◽  
pp. 30-53 ◽  
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.

Stochastic long-term behavior of a reinforced concrete arch bridge

2017 ◽  
Vol 20 (10) ◽  
pp. 1560-1571 ◽  
Author(s):  
Cheng Yang ◽  
Tianyu Xiang ◽  
Bing Du
Keyword(s):  
Reinforced Concrete ◽  
Hybrid Approach ◽  
Objective Evaluation ◽  
Monte Carlo Sampling ◽  
Steel Tube ◽  
Sensitivity Analyses ◽  
Arch Bridge ◽  
Input Variables ◽  
Creep And Shrinkage

Beipanjiang Bridge, a railway-reinforced concrete arch bridge with span of 445 m, is located in Guizhou, China. The self-shored technology with a frame of concrete-filled steel tube is applied in this construction, in which the frame performs as the falsework for casting concrete into arch ring and also serves as the major reinforcement of the permanent structure. The time-variant behaviors of long-span concrete arch bridge may be sensitive to the creep and shrinkage of concrete, especially when the staged construction is applied. Owing to the random property of concrete creep and shrinkage, the stochastic analysis is necessary to be employed in order to give a more rational and objective evaluation about the structural long-term behaviors. A hybrid approach including Monte Carlo sampling based on response surface method is developed in this study, based on which the statistical results of long-term deflection and time-variant stress are discussed. Finally, the stochastic contributions of input variables to structural responses are investigated with the sensitivity analyses.

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

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.

scholarly journals Forecasting ASEAN Tourist Arrivals in Malaysia using Different Time Series Models

2019 ◽  
Vol 8 (6S3) ◽  
pp. 572-578
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Moving Average ◽  
Percentage Error ◽  
Support Vector ◽  
Time Series Models ◽  
Mode Decomposition ◽  
Asean Country ◽  
Forecasting Performance

In this study three time series models are used for forecasting monthly ASEAN tourist arrivals in Malaysia from January 1999 to December 2015. Brunei, Thailand and Vietnam of ASEAN country selected as case study. This paper compares the forecasting accuracy of seasonal autoregressive integrated moving average (SARIMA), Support Vector Machine (SVM) and Wavelet Support Vector Machine (WSVM) and Empirical Mode Decomposition with Wavelet Support Vector Machine (EMD_WSVM) using root mean square error (RMSE) and mean absolute percentage error (MAPE) criterion. Moreover, correlation test has also been carried out to strengthen decisions, and to check accuracy of various forecasting models. Based on the forecasting performance of all four models, hybrid model SARIMA and EMD_WSVM are found to be best models as compare to single model SVM and hybrid model WSVM.

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

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

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.

Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach

2020 ◽  
Vol 45 (1) ◽  
pp. 52-67
Author(s):  
Don Chi Wai Wu ◽  
Lei Ji ◽  
Kaijian He ◽  
Kwok Fai Geoffrey Tso
Keyword(s):  
Short Term Memory ◽  
Moving Average ◽  
Hybrid Approach ◽  
Tourism Industry ◽  
Nonlinear Method ◽  
Monthly Basis ◽  
Autoregressive Integrated Moving Average ◽  
Forecast Technique ◽  
Tourist Demand

Timely predicting tourist demand is extremely important for the tourism industry. However, due to limited availability of data, most of the relevant research studies have focused on data on a quarterly or monthly basis. In this article, we propose a novel hybrid approach, SARIMA + LSTM, that is, seasonal autoregressive integrated moving average (SARIMA) combined with long short-term memory (LSTM) to forecast daily tourist arrivals to Macau SAR, China. The LSTM model is a novel artificial intelligence nonlinear method which has been shown to have the capacity to learn the long-term dependencies existing in the time series. SARIMA + LSTM benefits from the predictive power of the SARIMA model and the ability of the LSTM to further reduce residuals. The results show that the SARIMA + LSTM forecast technique outperforms other methods.

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