scholarly journals Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Sibtain ◽  
Xianshan Li ◽  
Ghulam Nabi ◽  
Muhammad Imran Azam ◽  
Hassan Bashir
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Prediction Models ◽  
Hybrid Models ◽  
Signal Decomposition ◽  
High Frequency Component ◽  
Support Vector ◽  
Two Stage ◽  
Runoff Prediction ◽  
Swat River

Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

scholarly journals Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

2021 ◽  
Author(s):  
Laleh Parviz ◽  
Kabir Rasouli ◽  
Ali Torabi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Model ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Highly Nonlinear

Abstract Precipitation forecast, especially on monthly and annual scales, is a key for optimal water resources management and planning, especially in semiarid climates with scarce water. The traditional hybrid models, in which two statistical models are used to separate and simulate linear and nonlinear components of precipitation time series, are still unable to provide accurate precipitation forecasts. This research aims to improve hybrid forecast models by combining one linear model and three nonlinear models with two preprocessing configurations: 1) using residuals of a linear model, representing the nonlinear component with different time steps and 2) using original time series of observations with different time steps, linear model simulations and residuals. Gene Expression Programming (GEP), Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) models were used individually as in the traditional hybrid models and combinedly as in the proposed hybrid models in this study. The performance of the hybrid models was improved by different methods such as inverse variance (Iv) as an error-based method, least square regression, genetic algorithm and SVR. Two weather stations of Tabriz (annual) and Rasht (monthly) in Iran were selected to test the developed models. The results showed that Theil’s coefficient, UII, decreased in configuration one for the Tabriz station by 9% and 15% for SVR and GMDH relative to GEP, suggesting that these two models performed better than GEP in the precipitation forecast. The error criteria used in developing the proposed hybrid models with all forecast combination methods better represent observations than the hybrid model. MSE decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station in configuration two when we combined the three models using GA to obtain the improved hybrid model relative to the hybrid model combined with SVR. Generally, the hybrid models when SVR, the error based methods and GA were incorporated showed better performance than traditional hybrid models. The developed models have implications for modeling highly nonlinear systems using full advantages of machine learning methods.

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.

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

A Hybrid Model of Fuzzy Time Series for Forecasting

2012 ◽  
Vol 433-440 ◽  
pp. 2694-2698
Author(s):  
Ju E Wang ◽  
Jian Zhong Qiao
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Fuzzy Time Series ◽  
Heuristic Model ◽  
University Of Alabama ◽  
Accuracy Rate ◽  
Forecasting Accuracy ◽  
Empirical Analyses

This study proposes a hybrid model for forecasting. The hybrid model is built on heuristic and weighted models of fuzzy time series. Compared to heuristic model, the hybrid model considers not only heuristic factors but also weighted factors. Hybrid model counts in more factors for dealing with forecasting problems to get a higher forecasting accuracy rate. The enrollment of University of Alabama is chosen as the forecasting targets. The empirical analyses show that the hybrid models provide better overall forecasting results than the previous models.

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

scholarly journals Prognosis of Chronic Renal Syndrome by Classification and Progression Using Temporal Abstraction

2019 ◽  
Vol 8 (11) ◽  
pp. 53-58
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Renal Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Series Data ◽  
Support Vector ◽  
Temporal Abstraction ◽  
Graphic Data ◽  
Lab Test

Chronic renal syndrome is defined as a progressive loss of renal function over period. Analysers have make effort in attempting to diagnosis the risk factors that may affect the retrogression of chronic renal syndrome. The motivation of this project helps to develop a prediction model for level 4 CKD patients to detect on condition that, their estimated Glomerular Filtration Rate (eGFR) stage downscale to lower than 15 ml/min/1.73 m². End phase renal disease, after six months accumulating their concluding lab test observation by assessing time affiliated aspects. Data mining algorithm along with Temporal Abstraction (TA) are confederated to reinforce CKD evolvement of prognostication models. In this work a inclusive of 112 chronic renal disease patients are composed from April 1952 to September 2011 which were extracted from the patient’s Electronic Medical Records (EMR). The information of chronic renal patients are collected in a big spatial info-graphic data. In order to analyse these info-graphic data, it is significant to detect the issues affecting CKD deterioration and hence it becomes a challenging task. To overcome this challenge, time series graph has been generated in this project work based on creatinine and albumin lab test values and reports of the time period. The presence of CKD diagnostic codes are transformed into default seven digit default format of International Classification of Disease 10 Clinical Modification (ICD 10 CM). Feature selection is performed in this work based on wrapper method using genetic algorithm. It is helpful for finding the most relevant variables for a predictive model. High Utility Sequential Rule Miner (HUSRM) is used here to address the discovery of CKD sequential rules based on sequence patterns. Temporal Abstraction (TA) techniques namely basic TA and complex TA are used in this work to analyse the status of chronic renal syndrome patients. Classification and Regression Technique (CART) along with Adaptive Boosting (AdaBoost) and Support Vector Machine Boosting (SVMBoost) are applied to develop the CKD in which the progression prediction models exhibit most accurate prediction. The results obtained from this work divulged that comprehending temporal observation forward the prognostic instances has escalated the efficacy of the instances. Finally, an evaluation metrics namely accuracy, sensitivity, specificity, positive likelihood, negative likelihood and Area Under the Curve (AUC) are helps to evaluate the performance of the prediction models which are designed and implemented in this project. Key Words: CKD, progression, time series data, genetic algorithm, sequential rules, TA classification and prediction model.

Comprehensive Data Analysis and Forecasting of Covid19 Outbreak in India (Preprint)

2020 ◽  
Author(s):  
Amit Thakur ◽  
Rajesh Singh ◽  
Anita Gehlot ◽  
Shaik Vaseem Akram ◽  
Prabin Kumar Das
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Input Data ◽  
Good Accuracy ◽  
Prediction Models ◽  
Indian Region ◽  
Support Vector ◽  
Rate Of Growth ◽  
Good Range

BACKGROUND COVID-19 is chronic based disease which is spreading with rapid pace in the entire world. Present study addresses the situation of outbreak of the COVID-19 disease in India and estimate the rise of the cases in India. This study addresses the present health infrastructure, infected health workforce clearly with the statistics. Support Vector Machine and Linear Regression are implemented in this study for predicting the expected cases. For the purpose of modelling, the input data of number of cases is considered from the march 15th , 2020. With the input data, the two models are trained for prediction of the cases. In the end, the results show that support vector machine and linear regression are giving good accuracy for prediction. OBJECTIVE The current studies aim to analyze and estimate the developments in the near future with reference to COVID-19 in India. The research is also planned to look at the preparation level of Indian government for this outbreak. The scope of the study is narrowed to build prediction models for the Indian region and uses SVMs for prediction methods based on time series that are easily built and readable under these crucial conditions. The study does not cover coverage of a COVID-19 outbreak for any other country. METHODS Support Vector Machine and Linear Regression are implemented in this study for predicting the expected cases. For the purpose of modelling, the input data of number of cases is considered from the march 15th , 2020. With the input data, the two models are trained for prediction of the cases. In the end, the results show that support vector machine and linear regression are giving good accuracy for prediction. RESULTS 1.Considering the change, the change in slope of the both curves in the graph, it can be concluded that the trained model is giving a quite good range of accuracy. 2.The Graph shows the plot of the predicted values and actual values fed during the testing of model. Considering the change, the change in slope of the both curves in the graph, it can be concluded that the trained model is giving a quite good range of accuracy. CONCLUSIONS In conclusion, the present work emphasized on presenting observations and predictions about COVID-19 outbreaks in the Indian region. Although the rate of growth at world level is not equal to the rate of growth, the situation appears dangerous as India is heading towards exponential growth. The expected patients are reaching in millions in the next 30 days by means of two separate time series forecasting models. With regard to the poor health facilities, it is going to difficult to combat the outbreak of virus without government addressing the effective measurements. Contrast to strict lockdown, social distancing, isolation, patient testing and medical care need to implement with war base for combating the pandemic in India. The forecasting in this study are still in beginning phases as the historical data is limit for creating reliable model. That to the risen of cases in India followed from the last 10 days so the training for the model may not be accurate, however the prediction model would be enhanced from existing models, as the greater number of medical and demographic data is available.Furthermore, even if the predictions are 60-70 percent correct, then the nation will also encounter this quite hard days.

Sign in / Sign up

Export Citation Format

Share Document