This study describes a new method for offline seizure detection using intracranial EEG ( iEEG ) . The proposed method integrated two interrelated steps : ( 1 ) establishing a decisional space on the basis of the interelectrode mean of the spectral power in the gamma frequencies after a thorough evaluation of temporal and frequency-based features and ( 2 ) constructing an artificial neural network that operated on this decisional space to delineate EEG files that contained seizures from those that did not . The data were obtained from 14 patients who underwent two-stage epilepsy surgery with subdural recordings . This paper proposes a solution of this problem based on the additional training of an artificial neural network in the course of the operation of a system for converting electrical energy from solar cells . An artificial neural network was trained on the initial current-voltage characteristics of an array of solar cells . The simulation was carried out taking into account the seasonal degradation of solar cells . Using the econometric model , this study uses data on Foreign Tourist Visits ( FTV ) , Google Trends Index ( GTI ) , Customer Price Index ( CPI ) and Exchange Rate ( ER ) . The forecasting method uses the Vector Error Correction Model ( VECM ) and then analyzes the model , provides forecasting and structural analysis of the model . The results of the model analysis are long-term and short-term analyzes and the results of forecasting evaluation show that the MAPE score is good . Its primary vehicle was the Journal of Forecasting and is now the International Journal of Forecasting . The Institute emphasizes empirical comparisons of reasonable forecasting approaches . Such studies can be used to identify the best forecasting procedures to use under given conditions , a process we call evidence-based forecasting . Exchange rate forecasting is an important problem . Several forecasting techniques have been proposed in order to gain some advantages . Most of them are either as good as random walk forecasting models or slightly worse . Each of the main forecast regions was broken down into smaller areas for more detailed analysis . The study found the forecast model is an effective forecast tool with Probability of Detection of dust storm occurrence exceeding 68 percent over Northern Africa , with a 16 percent False Alarm Rate . Southwest Asia forecasts had average Probability of Detection values of 61 percent with False Alarm Rates averaging 10 percent . By introducing Analytic Hierarchy Process to the model with three sub-models including land classification , Grey Model and population-economy regression model , the optimized multi-method model ( OMM ) can rationally improve the precision and the quality of being general . For illustration , an example of the main district of Xiangfan City is utilized and the case results show that the OMM has higher precision and more general applicability comparing to a single forecast tool mentioned above . The AHP-based OMM can effectively improve the weakness of theory flaw as well as precision and application deficiency by using only one forecast method . Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations . Therefore , the objective of this study was to investigate if regression kriging , as a geostatistical data fusion approach , can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate . Regression kriging might be suitable because we have a sparse data set ( ultrasound ) and an exhaustive data set ( UAV ) and both data sets have favorable properties for geostatistical analysis . Such techniques are , however , susceptible to the sort of spurious measurements produced by visual feature detectors , often resulting in an unrecoverable tracking failure . This paper investigates an alternative approach , where a minimal subset of the data provides the pose estimate , and a robust regression scheme selects the best subset . Bayesian inference in the regression stage combines measurements taken in one frame with predictions from previous frames , eliminating the need to further filter the pose estimates . Compared to the TCARI/SAVI model , the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc , with R2 and root-mean-square error ( RMSE ) values ranging from 0.68 to 0.80 and from 0.140 to 0.232 , respectively . Compared to Kc calculated from on-site measurements , the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops . Overall , use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale . In order to address these issues , we apply a two-stage methodology using flexible tree-based methods . We first run multilevel regression trees in the first stage , to estimate school value-added . In the second stage , we relate the estimated school value-added to school level variables by means of regression trees and boosting . The paper presents several command types of Unified Regression Statistical Analyses Model . The standard form of each function and the method of a non-linear function changed into linear one are given . First , we exploit three types of statistical features to quantify the degradation of SR images . Next , a cascade two-layer regression model , which integrates AdaBoost Decision Tree Regression and ridge regression , is trained to predict the quality of SR images in a coarse-to-fine manner . The experimental results demonstrate that the proposed method is superior to other previous SR quality evaluation approaches and shows better consistency with visual perception quality . Sensitivity analysis ( SA ) techniques were applied to study the significance of small alterations of field sizes ( segments ) on the IQM signal response . One hundred and eighty multileaf segments were analyzed with methods that include scatter plots ( SP ) , brute force , variance-based ( VAR ) , and standard regression coefficient SA . The segments were altered randomly within ±1 , ±2 , and ±3 mm leaf steps for 10 MV photon beams . This method requires to select a value in the parameter space at which the computation is performed . Bayesian inference for a change-point dynamic regression model and the computation of its marginal likelihood are explained . Motivated by results from three empirical illustrations , a simulation study shows that Chib ’s method is robust with respect to the choice of the parameter value used in the computations , among posterior mean , mode and quartiles . It is known that the existing $ $ \nu$$ ν -twin support vector regression ( $ $ \nu$$ ν -TWSVR ) has the ability to optimize $ $ \varepsilon _ 1$$ ε 1 and $ $ \varepsilon _ 2$$ ε 2 automatically through the proper selections of the parameters $ $ \nu _ 1$$ ν 1 and $ $ \nu _ 2$$ I employ an empirical illustration and a set of simulation studies to test the utility and feasibility of the proposed two-stage matching method . The results document the two-stage matching method 's conceptual appeal , but indicate that effect estimation under the two-stage matching method does not , in general , outperform more traditional matching-based or regression-based methods . Alternative specifications within the proposed method can improve performance of two-stage matching . Text coming from QA platforms is far less constrained compared to text from review specific platforms which current datasets are based on . We develop several strong baselines , relying on logistic regression and state - of - the - art recurrent neural networks . section : Introduction Finally , the output of global and local FCN are coupled together as the final score of the object . We also perform class - agnostic bounding box regression in a similar way . subsection : Local FCN Abstract We propose a novel multiclass classifier for single trial electroencephalogram ( EEG ) data in matrix form , namely multiclass support matrix machine ( MSMM ) , aiming at improving the classification accuracy of multiclass EEG signals , and hence enhancing the performance of EEG-based brain computer interfaces ( BCIs ) involving multiple mental activities . In order to construct the MSMM , we propose a novel objective function , which is composed of a multiclass hinge loss term and a combined regularization term . While the Frobenius norm prevents over-fitting when training the model , the nuclear norm captures the structural information within the matrix data . We further propose an efficient solver for MSMM based on the alternating direction method of multipliers ( ADMM ) framework . We conduct extensive experiments on two benchmark EEG datasets . ABSTRACT In this study , we extend the traditional monetary model and the random walk model with Markov-switching method and propose two new forecasting models called the Markov switching monetary model ( MSMM ) and Markov switching random walk model ( MSRW ) . Then , we evaluate the forecasting ability of these two new mixed models , MSMM and MSRW , and compare their performance with the traditional pure monetary model and pure Random walk model based on Mean Squared Forecast Error and Mean Absolute Forecast Error . The results show that the two hybrid models significantly improve the forecasting ability compared with the two traditional models in most scenarios . This paper presents an innovative prediction method for key performance indexes with multiple seasonal profiles . The proposed method , called Multiplicative Multi-Seasonal Model ( MSMM ) relies on a time series decomposition including multiple multiplicative seasonal profiles and a trend component . The method and its underlying model have been specifically designed to be computationally lightweight to scale to big-data scenarios envisaged in upcoming 5G-NFV environments . The MSMM performance has been evaluated on KPI traces of real operating infrastructures/services , made available by Yahoo ! The obtained results outlined how the MSMM prediction method provides more accurate forest than well-known algorithm like the seasonal version of ARIMA , with much reduced computational weight . Abstract This paper extends the traditional monetary model and the random walk model with Markov-switching method and proposes two new forecasting models called Markov switching monetary model ( MSMM ) and Markov switching random walk model ( MSRW ) . Compared with the traditional models , the evidence shows the two hybrid models perform better on forecasting the exchange rate . In this paper , the multiple sweep method of moments ( MSMM ) is employed to design non-uniform planar dipole antenna arrays in free space , where the genetic algorithm ( GA ) is integrated with the MSMM to find optimum array parameters . The antenna design is focused on parameters of non-uniform planar dipole antenna arrays , which are the length of each element , spacing between elements , and the feeding voltage of each element . Numerical results are presented to demonstrate the accuracy and efficiency of the MSMM/GA method for non-uniform planar dipole antenna arrays in free space . Four algorithms are given and applied to several real cases . The result shows the proposed method not only attains a forecasting accuracy comparable to that of the theoretically optimal combining methods , but also provides some valuable insights into combination methodology , which verifies that the combining forecast upgrades the forecasting performance . One of them , based on a model output statistics ( MOS ) forecast for the period July 2007 to December 2009 , is assessed and compared to the zero cost 2-day persistence approach , which assumes yesterday ’s weather conditions and electricity generation also for the following day . The quality of the meteorological forecasts is analyzed both with forecast verification methods and from the perspective of a power plant operator . Using MOS , penalties in the study period are reduced by 47.6 % compared to the 2-day persistence case . The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α-fair utility function of the cumulated rates of the users along their trajectories . Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler . In addition , we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration . Then , we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set . Through various experiments , we show that our hybrid forecast model performs better than other popular single forecast models . Very short-term load forecasting predicts load over one hour into the future in five minute steps and performs the moving forecast every five minutes . This is essential for area generation control and resource dispatch , and helps operators make good decisions . Very short-term load forecasting predicts the load over one hour into the future in five-minute steps and performs the moving forecast every five minutes . To quantify forecasting accuracy , the confidence interval is estimated in real-time . The invention discloses a RTK positioning precision forecast method and a system which are characterized by : acquiring ionosphere TEC forecast information ; acquiring a RTK precision model which is established on the RTK positioning precision information and ionosphere TEC information ; the RTK positioning precision information including time , baseline , and RTK positioning precision ; the ionosphere TEC information including the time , the baseline and ionosphere TEC ; based on the ionosphere TEC forecast information and the RTK precision model , determining RTK forecast precision . According to the invention , through the ionosphere TEC forecast information and the RTK precision model , the RTK positioning precision forecast method and system enable users to predict RTK positioning precision based on the ionosphere TEC forecast information and obtain a prediction result which is the RTK forecast precision of time to be forecasted , so that further processing can be conducted based on the prediction result . The present paper proposes an algorithm of loads operation planning , by means of a relay unit , of a PVG/Battery installation composed of three load types : light , water pump and cold room . The planning strategy was based on the PVG generation dynamic forecast using least squares approach . Starting at sunrise , the generated energy was forecasted based on the outcome of the last 15 days . We try to answer how far ahead a reliable wind forecast is possible , and how much information from the past is necessary . We demonstrate the capabilities of SVR-based wind energy forecast on the micro-scale level of one wind grid point , and on the larger scale of a whole wind park . The selected days from each season are a clear day ( CD ) , partial cloudy day ( PCD ) and cloudy day ( CLD ) . The proposed forecast framework provides a reduction in forecast nRMSE in seasonal daily and week ahead case studies . Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification ( e.g. , the eruption strength or vertical distribution of the emitted particles ) , with consequent implications for an operational ash impact assessment . We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters . The FALL3D atmospheric dispersal model is coupled with the ensemble transform Kalman filter ( ETKF ) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of the 3-D ash concentration and source parameters . The skill of Artificial Intelligence (AI)-based computational mechanisms to model important nonlinear hydrological processes is addressed in this chapter . Three major themes are illustrated : ( 1 ) conventional data-based nonlinear concepts such as Box and Jenkins Models , ARX , ARIMAX , and intelligent computing tools such as LLR , ANN , ANFIS , and SVMs ; ( 2 ) the discrete wavelet transform ( DWT ) , a powerful signal processing tool and its application in hydrology , and ( 3 ) conjunction models of DWT , namely neuro-wavelet models , Wavelet-ANFIS models , and Wavelet-SVM s. This chapter gives a detailed description of the training algorithms used in this book and points out the conceptual advantages of Levenberg – Marquardt ( LM ) algorithms over Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) training algorithms and Conjugate Gradient ( CG ) training algorithms . The following sections are included : introduction ; smoothing methods of prediction ; box and Jenkins method ; other selected methods ; and concluding remarks . This article describes how the data is managed ( collection by modem , plausibility tests , filling blanks in the weather files ) , how the data flow from the Agrometeorological Measuring Net Rheinland-Pfalz ( AMN RP ) to the adviser in the plant protection office is organized , how administration of data , sensors and service routines works and , finally , the conclusions and improvements that are made based on the results . This leads to meteorological data of high quality , which are necessary for weather-based forecasting systems to guarantee a high standard of certainty when using the results of simulation in agricultural practice . A Virtual Weather Station Network for Agricultural Decision Support Systems in Wallonia , South of Belgium . Weather-based forecasting models play a major role in agricultural decision support systems but warnings are usually computed at regional level due to a limited amount of automatic weather stations . Farmers have to refer to the nearest AWS but recommendations are not always adapted to their situation . Early detection of pest and its control is one of the aspects of IPM . Weather based forecasting is well accepted method for this . Various meteorological data like-temperature , humidity , leaf wetness duration ( LWD ) plays the vital roles in the growth of microorganism responsible for disease . The current manuscript proposes the combination of two methods , or ‘ tools ’ , for timeline analyses : Matrix Forecasting and Behaviour Sequence Analysis ( BSA ) . Matrix Forecasting provides a clear and comprehensive approach to outlining predictions investigators make , the rationale underlying these predictions , the accuracy , and the evidence . Matrix Forecasting also outlines areas for future investigation , for example , if new technology becomes available or new test results are returned . The BSA provides a statistical , visual pathway map that outlines the proposed or proven steps in a crime . The combination of these methods provides a new approach to mapping criminal investigations and has been effectively used in several real-world cold case reviews . To illustrate the benefits of this combined approach , a real-world example , the Jeffrey MacDonald , aka Green Beret Killer case , will be analysed using Matrix Forecasting and BSA to show the benefits of the method in terms of providing a quick-guide for future review and solvability factors . Comparatively , numerical modeling shows more extensive applications and more potential in fog prediction . Under the current computer condition , coupling of high-resolution one-dimensional fog model with three-dimensional mesoscale weather forecasting model shows better forecasting capacity , compared with the mesoscale model alone . The coupled method is much feasible for fog prediction in airport and along expressway , which are prone to heavy fog and the observations are already available . The four specifications of the density functions are dynamic and conditional upon exogenous drivers , thereby permitting the location , scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts . The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function , following the closed-form analytical solutions of the cumulative distribution functions . Colorado State University ( CSU ) has developed more than 160 CSIP-based natural resource model and data services that can be integrated into an organization ’s information system analysing environmental concerns . Model service examples include water and wind erosion prediction ( WEPP , WEPS , RUSLE2 ) , soil quality ( SCI , STIR ) , water quality ( SWAT-CP , WQM , CFA , AgES-W ) , stream degradation ( SWAT-DEG ) , hydrology ( PRMS , NRCS Hydrotools , CFA , Water Supply Forecasting ) , hydraulics ( Cross-Section ) , and grazing management ( GRAS ) . Available supporting data services and sources include soil ( SSURGO ) , ecological site ( ESIS ) , climate ( CLIGEN , WINDGEN , PRISM ) , land management and crop rotations ( LMOD ) , and pesticides ( WQM ) . Unlike similar approaches in prior literature ( see e.g. , ( A.E. Bryson and Y. Ho , 1975 ) , ( F.L. Lewis , 1986 ) ) , our approach is capable of enforcing piecewise state and control constraints . We describe how the basic approach can be integrated within the framework of forecasting MPC ( FMPC ) and illustrate its effectiveness by means of an example . A study of a public utility showed demand distributions in each category to be of a specific nature . Stock-control and forecasting methods are developed , and simulation tests are described which compare these methods with ( for ease of comparison ) the assumption of continuous demand . Abstract Singular spectrum analysis ( SSA ) has become a popular and widely used forecasting and pre-processing technique in time series analysis which is currently exploited in chemical process monitoring and fault detection . Given its increased application and superior performance in comparison to conventional multivariate methods such as Principal Component Analysis ( PCA ) and Wavelets and its nonlinear extensions , it is relevant to study the variants of SSA and its applications in process monitoring . Log-periodic power law ( LPPL ) is one state-of-the-art method to predict turning points . In this research , we propose an improved version of LPPL forecasting model by incorporating a method called multi-population genetic algorithm ( MPGA ) to search for optimal values of parameters in the LPPL model . By doing so , the improved LPPL model provided significantly superior performance in predicting the turning points compared to prior researches . To address these limitations , we formulate an optimisation problem with respect to time-varying utility under the given hardware constraints . We also present PREACT , an online energy-management algorithm that approximates the optimal solution to the optimisation problem by incorporating long-term energy forecasting .
This study describes a new method for offline seizure detection using intracranial EEG ( iEEG ) . The proposed method integrated two interrelated steps : ( 1 ) establishing a decisional space on the basis of the interelectrode mean of the spectral power in the gamma frequencies after a thorough evaluation of temporal and frequency-based features and ( 2 ) constructing an artificial neural network that operated on this decisional space to delineate EEG files that contained seizures from those that did not . The data were obtained from 14 patients who underwent two-stage epilepsy surgery with subdural recordings .
This paper proposes a solution of this problem based on the additional training of an artificial neural network in the course of the operation of a system for converting electrical energy from solar cells . An artificial neural network was trained on the initial current-voltage characteristics of an array of solar cells . The simulation was carried out taking into account the seasonal degradation of solar cells .
Using the econometric model , this study uses data on Foreign Tourist Visits ( FTV ) , Google Trends Index ( GTI ) , Customer Price Index ( CPI ) and Exchange Rate ( ER ) . The forecasting method uses the Vector Error Correction Model ( VECM ) and then analyzes the model , provides forecasting and structural analysis of the model . The results of the model analysis are long-term and short-term analyzes and the results of forecasting evaluation show that the MAPE score is good .
Its primary vehicle was the Journal of Forecasting and is now the International Journal of Forecasting . The Institute emphasizes empirical comparisons of reasonable forecasting approaches . Such studies can be used to identify the best forecasting procedures to use under given conditions , a process we call evidence-based forecasting .
Exchange rate forecasting is an important problem . Several forecasting techniques have been proposed in order to gain some advantages . Most of them are either as good as random walk forecasting models or slightly worse .
Each of the main forecast regions was broken down into smaller areas for more detailed analysis . The study found the forecast model is an effective forecast tool with Probability of Detection of dust storm occurrence exceeding 68 percent over Northern Africa , with a 16 percent False Alarm Rate . Southwest Asia forecasts had average Probability of Detection values of 61 percent with False Alarm Rates averaging 10 percent .
By introducing Analytic Hierarchy Process to the model with three sub-models including land classification , Grey Model and population-economy regression model , the optimized multi-method model ( OMM ) can rationally improve the precision and the quality of being general . For illustration , an example of the main district of Xiangfan City is utilized and the case results show that the OMM has higher precision and more general applicability comparing to a single forecast tool mentioned above . The AHP-based OMM can effectively improve the weakness of theory flaw as well as precision and application deficiency by using only one forecast method .
Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations . Therefore , the objective of this study was to investigate if regression kriging , as a geostatistical data fusion approach , can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate . Regression kriging might be suitable because we have a sparse data set ( ultrasound ) and an exhaustive data set ( UAV ) and both data sets have favorable properties for geostatistical analysis .
Such techniques are , however , susceptible to the sort of spurious measurements produced by visual feature detectors , often resulting in an unrecoverable tracking failure . This paper investigates an alternative approach , where a minimal subset of the data provides the pose estimate , and a robust regression scheme selects the best subset . Bayesian inference in the regression stage combines measurements taken in one frame with predictions from previous frames , eliminating the need to further filter the pose estimates .
Compared to the TCARI/SAVI model , the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc , with R2 and root-mean-square error ( RMSE ) values ranging from 0.68 to 0.80 and from 0.140 to 0.232 , respectively . Compared to Kc calculated from on-site measurements , the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops . Overall , use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale .
In order to address these issues , we apply a two-stage methodology using flexible tree-based methods . We first run multilevel regression trees in the first stage , to estimate school value-added . In the second stage , we relate the estimated school value-added to school level variables by means of regression trees and boosting .
The paper presents several command types of Unified Regression Statistical Analyses Model . The standard form of each function and the method of a non-linear function changed into linear one are given .
First , we exploit three types of statistical features to quantify the degradation of SR images . Next , a cascade two-layer regression model , which integrates AdaBoost Decision Tree Regression and ridge regression , is trained to predict the quality of SR images in a coarse-to-fine manner . The experimental results demonstrate that the proposed method is superior to other previous SR quality evaluation approaches and shows better consistency with visual perception quality .
Sensitivity analysis ( SA ) techniques were applied to study the significance of small alterations of field sizes ( segments ) on the IQM signal response . One hundred and eighty multileaf segments were analyzed with methods that include scatter plots ( SP ) , brute force , variance-based ( VAR ) , and standard regression coefficient SA . The segments were altered randomly within ±1 , ±2 , and ±3 mm leaf steps for 10 MV photon beams .
This method requires to select a value in the parameter space at which the computation is performed . Bayesian inference for a change-point dynamic regression model and the computation of its marginal likelihood are explained . Motivated by results from three empirical illustrations , a simulation study shows that Chib ’s method is robust with respect to the choice of the parameter value used in the computations , among posterior mean , mode and quartiles .
It is known that the existing $ $ \nu$$ ν -twin support vector regression ( $ $ \nu$$ ν -TWSVR ) has the ability to optimize $ $ \varepsilon _ 1$$ ε 1 and $ $ \varepsilon _ 2$$ ε 2 automatically through the proper selections of the parameters $ $ \nu _ 1$$ ν 1 and $ $ \nu _ 2$$
I employ an empirical illustration and a set of simulation studies to test the utility and feasibility of the proposed two-stage matching method . The results document the two-stage matching method 's conceptual appeal , but indicate that effect estimation under the two-stage matching method does not , in general , outperform more traditional matching-based or regression-based methods . Alternative specifications within the proposed method can improve performance of two-stage matching .
Text coming from QA platforms is far less constrained compared to text from review specific platforms which current datasets are based on . We develop several strong baselines , relying on logistic regression and state - of - the - art recurrent neural networks . section : Introduction
Finally , the output of global and local FCN are coupled together as the final score of the object . We also perform class - agnostic bounding box regression in a similar way . subsection : Local FCN
Abstract We propose a novel multiclass classifier for single trial electroencephalogram ( EEG ) data in matrix form , namely multiclass support matrix machine ( MSMM ) , aiming at improving the classification accuracy of multiclass EEG signals , and hence enhancing the performance of EEG-based brain computer interfaces ( BCIs ) involving multiple mental activities . In order to construct the MSMM , we propose a novel objective function , which is composed of a multiclass hinge loss term and a combined regularization term .
While the Frobenius norm prevents over-fitting when training the model , the nuclear norm captures the structural information within the matrix data . We further propose an efficient solver for MSMM based on the alternating direction method of multipliers ( ADMM ) framework . We conduct extensive experiments on two benchmark EEG datasets .
ABSTRACT In this study , we extend the traditional monetary model and the random walk model with Markov-switching method and propose two new forecasting models called the Markov switching monetary model ( MSMM ) and Markov switching random walk model ( MSRW ) . Then , we evaluate the forecasting ability of these two new mixed models , MSMM and MSRW , and compare their performance with the traditional pure monetary model and pure Random walk model based on Mean Squared Forecast Error and Mean Absolute Forecast Error . The results show that the two hybrid models significantly improve the forecasting ability compared with the two traditional models in most scenarios .
This paper presents an innovative prediction method for key performance indexes with multiple seasonal profiles . The proposed method , called Multiplicative Multi-Seasonal Model ( MSMM ) relies on a time series decomposition including multiple multiplicative seasonal profiles and a trend component . The method and its underlying model have been specifically designed to be computationally lightweight to scale to big-data scenarios envisaged in upcoming 5G-NFV environments . The MSMM performance has been evaluated on KPI traces of real operating infrastructures/services , made available by Yahoo ! The obtained results outlined how the MSMM prediction method provides more accurate forest than well-known algorithm like the seasonal version of ARIMA , with much reduced computational weight .
Abstract This paper extends the traditional monetary model and the random walk model with Markov-switching method and proposes two new forecasting models called Markov switching monetary model ( MSMM ) and Markov switching random walk model ( MSRW ) . Compared with the traditional models , the evidence shows the two hybrid models perform better on forecasting the exchange rate .
In this paper , the multiple sweep method of moments ( MSMM ) is employed to design non-uniform planar dipole antenna arrays in free space , where the genetic algorithm ( GA ) is integrated with the MSMM to find optimum array parameters . The antenna design is focused on parameters of non-uniform planar dipole antenna arrays , which are the length of each element , spacing between elements , and the feeding voltage of each element . Numerical results are presented to demonstrate the accuracy and efficiency of the MSMM/GA method for non-uniform planar dipole antenna arrays in free space .
Four algorithms are given and applied to several real cases . The result shows the proposed method not only attains a forecasting accuracy comparable to that of the theoretically optimal combining methods , but also provides some valuable insights into combination methodology , which verifies that the combining forecast upgrades the forecasting performance .
One of them , based on a model output statistics ( MOS ) forecast for the period July 2007 to December 2009 , is assessed and compared to the zero cost 2-day persistence approach , which assumes yesterday ’s weather conditions and electricity generation also for the following day . The quality of the meteorological forecasts is analyzed both with forecast verification methods and from the perspective of a power plant operator . Using MOS , penalties in the study period are reduced by 47.6 % compared to the 2-day persistence case .
The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α-fair utility function of the cumulated rates of the users along their trajectories . Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler .
In addition , we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration . Then , we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set . Through various experiments , we show that our hybrid forecast model performs better than other popular single forecast models .
Very short-term load forecasting predicts load over one hour into the future in five minute steps and performs the moving forecast every five minutes . This is essential for area generation control and resource dispatch , and helps operators make good decisions .
Very short-term load forecasting predicts the load over one hour into the future in five-minute steps and performs the moving forecast every five minutes . To quantify forecasting accuracy , the confidence interval is estimated in real-time .
The invention discloses a RTK positioning precision forecast method and a system which are characterized by : acquiring ionosphere TEC forecast information ; acquiring a RTK precision model which is established on the RTK positioning precision information and ionosphere TEC information ; the RTK positioning precision information including time , baseline , and RTK positioning precision ; the ionosphere TEC information including the time , the baseline and ionosphere TEC ; based on the ionosphere TEC forecast information and the RTK precision model , determining RTK forecast precision . According to the invention , through the ionosphere TEC forecast information and the RTK precision model , the RTK positioning precision forecast method and system enable users to predict RTK positioning precision based on the ionosphere TEC forecast information and obtain a prediction result which is the RTK forecast precision of time to be forecasted , so that further processing can be conducted based on the prediction result .
The present paper proposes an algorithm of loads operation planning , by means of a relay unit , of a PVG/Battery installation composed of three load types : light , water pump and cold room . The planning strategy was based on the PVG generation dynamic forecast using least squares approach . Starting at sunrise , the generated energy was forecasted based on the outcome of the last 15 days .
We try to answer how far ahead a reliable wind forecast is possible , and how much information from the past is necessary . We demonstrate the capabilities of SVR-based wind energy forecast on the micro-scale level of one wind grid point , and on the larger scale of a whole wind park .
The selected days from each season are a clear day ( CD ) , partial cloudy day ( PCD ) and cloudy day ( CLD ) . The proposed forecast framework provides a reduction in forecast nRMSE in seasonal daily and week ahead case studies .
Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification ( e.g. , the eruption strength or vertical distribution of the emitted particles ) , with consequent implications for an operational ash impact assessment . We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters . The FALL3D atmospheric dispersal model is coupled with the ensemble transform Kalman filter ( ETKF ) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of the 3-D ash concentration and source parameters .
The skill of Artificial Intelligence (AI)-based computational mechanisms to model important nonlinear hydrological processes is addressed in this chapter . Three major themes are illustrated : ( 1 ) conventional data-based nonlinear concepts such as Box and Jenkins Models , ARX , ARIMAX , and intelligent computing tools such as LLR , ANN , ANFIS , and SVMs ; ( 2 ) the discrete wavelet transform ( DWT ) , a powerful signal processing tool and its application in hydrology , and ( 3 ) conjunction models of DWT , namely neuro-wavelet models , Wavelet-ANFIS models , and Wavelet-SVM s. This chapter gives a detailed description of the training algorithms used in this book and points out the conceptual advantages of Levenberg – Marquardt ( LM ) algorithms over Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) training algorithms and Conjugate Gradient ( CG ) training algorithms .
The following sections are included : introduction ; smoothing methods of prediction ; box and Jenkins method ; other selected methods ; and concluding remarks .
This article describes how the data is managed ( collection by modem , plausibility tests , filling blanks in the weather files ) , how the data flow from the Agrometeorological Measuring Net Rheinland-Pfalz ( AMN RP ) to the adviser in the plant protection office is organized , how administration of data , sensors and service routines works and , finally , the conclusions and improvements that are made based on the results . This leads to meteorological data of high quality , which are necessary for weather-based forecasting systems to guarantee a high standard of certainty when using the results of simulation in agricultural practice .
A Virtual Weather Station Network for Agricultural Decision Support Systems in Wallonia , South of Belgium . Weather-based forecasting models play a major role in agricultural decision support systems but warnings are usually computed at regional level due to a limited amount of automatic weather stations . Farmers have to refer to the nearest AWS but recommendations are not always adapted to their situation .
Early detection of pest and its control is one of the aspects of IPM . Weather based forecasting is well accepted method for this . Various meteorological data like-temperature , humidity , leaf wetness duration ( LWD ) plays the vital roles in the growth of microorganism responsible for disease .
The current manuscript proposes the combination of two methods , or ‘ tools ’ , for timeline analyses : Matrix Forecasting and Behaviour Sequence Analysis ( BSA ) . Matrix Forecasting provides a clear and comprehensive approach to outlining predictions investigators make , the rationale underlying these predictions , the accuracy , and the evidence . Matrix Forecasting also outlines areas for future investigation , for example , if new technology becomes available or new test results are returned . The BSA provides a statistical , visual pathway map that outlines the proposed or proven steps in a crime . The combination of these methods provides a new approach to mapping criminal investigations and has been effectively used in several real-world cold case reviews . To illustrate the benefits of this combined approach , a real-world example , the Jeffrey MacDonald , aka Green Beret Killer case , will be analysed using Matrix Forecasting and BSA to show the benefits of the method in terms of providing a quick-guide for future review and solvability factors .
Comparatively , numerical modeling shows more extensive applications and more potential in fog prediction . Under the current computer condition , coupling of high-resolution one-dimensional fog model with three-dimensional mesoscale weather forecasting model shows better forecasting capacity , compared with the mesoscale model alone . The coupled method is much feasible for fog prediction in airport and along expressway , which are prone to heavy fog and the observations are already available .
The four specifications of the density functions are dynamic and conditional upon exogenous drivers , thereby permitting the location , scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts . The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function , following the closed-form analytical solutions of the cumulative distribution functions .
Colorado State University ( CSU ) has developed more than 160 CSIP-based natural resource model and data services that can be integrated into an organization ’s information system analysing environmental concerns . Model service examples include water and wind erosion prediction ( WEPP , WEPS , RUSLE2 ) , soil quality ( SCI , STIR ) , water quality ( SWAT-CP , WQM , CFA , AgES-W ) , stream degradation ( SWAT-DEG ) , hydrology ( PRMS , NRCS Hydrotools , CFA , Water Supply Forecasting ) , hydraulics ( Cross-Section ) , and grazing management ( GRAS ) . Available supporting data services and sources include soil ( SSURGO ) , ecological site ( ESIS ) , climate ( CLIGEN , WINDGEN , PRISM ) , land management and crop rotations ( LMOD ) , and pesticides ( WQM ) .
Unlike similar approaches in prior literature ( see e.g. , ( A.E. Bryson and Y. Ho , 1975 ) , ( F.L. Lewis , 1986 ) ) , our approach is capable of enforcing piecewise state and control constraints . We describe how the basic approach can be integrated within the framework of forecasting MPC ( FMPC ) and illustrate its effectiveness by means of an example .
A study of a public utility showed demand distributions in each category to be of a specific nature . Stock-control and forecasting methods are developed , and simulation tests are described which compare these methods with ( for ease of comparison ) the assumption of continuous demand .
Abstract Singular spectrum analysis ( SSA ) has become a popular and widely used forecasting and pre-processing technique in time series analysis which is currently exploited in chemical process monitoring and fault detection . Given its increased application and superior performance in comparison to conventional multivariate methods such as Principal Component Analysis ( PCA ) and Wavelets and its nonlinear extensions , it is relevant to study the variants of SSA and its applications in process monitoring .
Log-periodic power law ( LPPL ) is one state-of-the-art method to predict turning points . In this research , we propose an improved version of LPPL forecasting model by incorporating a method called multi-population genetic algorithm ( MPGA ) to search for optimal values of parameters in the LPPL model . By doing so , the improved LPPL model provided significantly superior performance in predicting the turning points compared to prior researches .
To address these limitations , we formulate an optimisation problem with respect to time-varying utility under the given hardware constraints . We also present PREACT , an online energy-management algorithm that approximates the optimal solution to the optimisation problem by incorporating long-term energy forecasting .
Subdural EEG Classification Into Seizure and Nonseizure Files Using Neural Networks in the Gamma Frequency Band Optimization of Artificial Neural Network Learning for Maximum Power Point Tracking After the Degradation of the Solar Battery Data Analytics For Forecasting Arrival of Tourism Visit in Indonesia Making Progress in Forecasting A hybrid model for exchange rate prediction Forecasting dust storms using the CARMA-dust model and MM5 weather data Optimized Multi-Method Model for Construction Land Demand Forecast: A Case Study of the Main District of Xiangfan City Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery Fast visual tracking by temporal consensus Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices Student and school performance across countries: A machine learning approach The application of unified regression statistical analyses model in land utilization dynamic measure Learning a Cascade Regression for No-Reference Super-Resolution Image Quality Assessment Sensitivity Analysis of the Integral Quality Monitoring System® Using Monte Carlo Simulation On Marginal Likelihood Computation in Change-Point Models An adaptive twin support vector regression machine based on rough and fuzzy set theories Using a Two-Stage Propensity Score Matching Strategy and Multilevel Modeling to Estimate Treatment Effects in a Multisite Observational Study SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods CoupleNet: Coupling Global Structure with Local Parts for Object Detection Multiclass support matrix machine for single trial EEG classification Do the Markov Switching-based Hybrid Models Perform Better in Forecasting Exchange Rates? A Lightweight Prediction Method for Scalable Analytics of Multi-seasonal KPIs Can Markov Switching Based Hybrid Models Improve the Performance of Forecasting Exchange Rates? The MSMM/GA design of non-uniform planar dipole antenna arrays SELECTING AND COMBINING ROBUST FORECASTING MODELS USING RULES BASED ON VALIDITY AND RELIABILITY Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market Forecast scheduling for mobile users Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron Interacting multiple model approach for very short-term load forecasting and confidence interval estimation Hybrid Kalman algorithms for very short-term load forecasting and confidence interval estimation RTK positioning precision forecast method and system Dynamic forecasting-based load control of an autonomous photovoltaic installation Short-Term Wind Energy Forecasting Using Support Vector Regression A multivariate ensemble framework for short term solar photovoltaic output power forecast Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0) Machine Learning and Artificial Intelligence-Based Approaches Some popular methods of prediction Collection, processing and transfer of weather data in the Agrometeorological Measuring Net of Rheinland‐Pfalz: a prior condition for using weather‐based forecasting systems* The Agromet Project: A Virtual Weather Station Network for Agricultural Decision Support Systems in Wallonia, South of Belgium Weather based plant diseases forecasting using fuzzy logic Matrix Forecasting and Behaviour Sequence Analysis: Part of the Timeline Toolkit for Criminal Investigation Progress in fog prediction Forecasting the Intra-Day Spread Densities of Electricity Prices Cloud Services Integration Platform (CSIP) Model and Data Services Model Predictive Control for Constrained Discrete Time Systems: An Optimal Perturbation Analysis Approach Stock Control with Sporadic and Slow-Moving Demand Fault Detection of Tennessee Eastman Process using Kernel Dissimilarity Scale Based Singular Spectrum Analysis The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm Poster Abstract: Long-Term Energy-Neutral Operation of Solar Energy-Harvesting Sensor Nodes under Time-Varying Utility
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ICIP COLING 2017 IEEE International Conference on Computer Vision (ICCV) TIWDC ITQM PIMRC SOCO ADHOC-NOW ACC IPSN
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