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Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using a newly defined metric – area...
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site...
Solar irradiance prediction has a significant impact on various aspects of power system generation. The predictive models can be deployed to improve the planning and operation of renewable systems and can improve the power purchase process and bring several advantages to the power utilities. The irradiance is affected by several factors, such as clouds and dust, and it becomes challenging for physical...
Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality...
Electricity consumption prediction is an important but demanding issue in the study of power systems. It is difficult for the conventional prediction methods, such as linear models, to utilize relevant domain knowledge in the forecasting of power peaks. In this paper, we propose an approach merging a regression predictor and a peak compensator together. The latter is designed to compensate for the...
Medical institutes use Electronic Medical Record (EMR) to record a series of medical events, including diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Plenty of data mining technologies are applied in the EMR data set for knowledge discovery, which is precious to medical practice. The knowledge found is conducive to develop treatment plans, improve...
This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also...
Accurate prediction of the future locations of the host vehicle as well as that of the surrounding objects is one of the key challenges in improving road traffic safety. The traditional approach for this task has been using physics-based motion models such as kinematic and dynamic models, the result of which is not reliable for long-term prediction. In this paper, we present simulation results demonstrating...
Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer “is there an equal number of balls and boxes?” we can look for balls, look for boxes, count them, and compare the results. The recently proposed Neural Module Network (NMN) architecture [3, 2] implements this approach to...
Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships...
To implement efficient control solutions for energy micro-systems a key issue is to predict the difference between the power generated by renewable resources and the load determined by local consumers. This paper explores linear, based on AR and ARMA models, and nonlinear solutions, based on neural networks, for predicting the power balance in a micro-grid. The predictions can be used at the top level...
Despite the recent advancements in glycemic control for diabetic patients, the realization of an automated closed-loop artificial pancreas is still a challenge. The purpose of this research is to develop an integrated control system for in silico closed loop administration of insulin for Type 1 diabetic patients based on patients' medical record and real-time control-relevant data. The proposed system...
Short-term forecasting tool for wind power generation (WPG) can effectively enhance the scheduling of dispatchable generation and avoid wind power (WP) curtailment. This paper presents a Group Method of Data Handling (GMDH)-Neural Network (NN) approach for forecasting wind speed (Ws) and wind power output (WPO) in the short-term. Wavelet denoising is used to filter the high frequency outliers in the...
Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation...
This paper presents a prediction model to predict biogas production for anaerobic digestion process of food waste. There exist sophisticated biogas production models in published literature, but the application of these models in anaerobic digestion process of food waste is often impractical because of its high complexity. Considering the shortcomings of the accuracy of these biogas production prediction...
The Escherichia coli (E. coli, ATCC 25922), Staphylococcus aureus (S. aureus, ATCC 29213), and Salmonella (SE, ATCC 14028) are three common bacterial pathogens of BSIs (Bloodstream infection). Accurately identifying these three bacterial pathogens will greatly help doctors to reduce the number of days to cure the patients. In this study, the identification models for bloodstream infection are studied...
This paper proposes a development's prediction model based on Artificial Neural Network. The methodology proposed consists in: i) countries selection; ii) selection and obtainment of indicators referring to the selected countries; iii) proposal prediction model; iv) Artificial Neural Network training and validation. The results indicated predicted values close to the real values of the Brazilian indicators...
This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and...
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