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This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel...
Demand forecasting for replenishment is one of the main issue for retail industry in terms of optimizing stocks, minimizing costs and also for reducing stock out problem. Better forecasting for demands, means maximizing sales and result with more revenue and profit for retailers. An other critical result of the stock out problem is of course dissatisfied customers and customer churn effect to retailers...
In this study, we propose an ensemble learning architecture called "Cognitive Learner", for classification of cognitive states from functional magnetic resonance imaging (fMRI). Proposed architecture consists of a two-layer hierarchy. In the first layer, called voxel layer, we model the connectivity among the voxel time series to represent the detailed information about the experiment. In...
Workload prediction in computing systems like Cloud and Grid is an essential prerequisite for successful load balancing and achieving service-level agreements. However, since workloads in different systems and architectures have varied characteristics, providing an accurate single prediction model can be very challenging. Therefore, in this paper we have designed and implemented a model of stacking...
Currency exchange rates forecasting is paid a considerable attention of the researchers in the field of forecasting. The neural network is a well-known tool in machine learning. However, two issues are always interested by the scientists: getting toward to global convergence of extreme solutions and determining the optimal weight of the network. This paper proposes the multi-objective method of ensemble...
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal...
Fuzzy time series has been utilized to solve the problem of time series prediction in diverse fields. In the fuzzy time series prediction, the fuzzy logical relations have great impacts on the prediction performance. To obtain the exact and complex fuzzy logical relations between the fuzzy variables for the time series prediction, we propose to extract them from the historical data and model them...
Time series predicting has become an important issue in many fields. The prediction methods which are based on the extreme learning machines have attracted many researchers. However, the predicted results of the extreme learning machines have some randomness. To obtain the better predicting performance and improve the randomness, we propose a new adaptive ensemble model of extreme learning machines...
Modern airlines use sophisticated pricing models to maximize their revenue, which results in highly volatile airfares. Without sufficient information, it is usually difficult for ordinary customers to estimate future price changes. Over the last few years, several studies have tried to solve the problem of optimal purchase timing for flight tickets, in which the prediction task is described as a binary...
In this paper we present a general procedure to use Bagging techniques for time series processing and forecasting problems Bagging is one of the most used techniques for combining several predictors in order to produce a highly accurate method. The method uses bootstrap replications of the original training set and for each replicate sample one predictor is generated. After that the method combines...
Time Series Forecasting is an area being concerned by the researchers. Recently, many time series forecasting solutions have been given, but the forecasting accuracy of these solutions needs to be taken into consideration. In this paper, we propose an evolutionary ensemble-based model and experimented it on four sets of test data on exchange rates (USD, NZD, USD and YEN) against the AUD. We applied...
Increasing availability of multi-scale physiological data opens new horizons for quantitative modeling in biomedical applications. However, practical limitations of existing approaches include both the low accuracy of the simplified analytical models and empirical expert-defined rules and the insufficient interpretability and stability of the pure data-driven models. Recently it was shown that generic...
Sales prediction is an important problem for different companies involved in manufacturing, logistics, marketing, wholesaling and retailing. Food companies are more concerned with sales prediction of products having a short shelf-life and seasonal changes in demand. The demand may depend on many hidden contexts, not given explicitly in the form of predictive features. Even if some changes are known...
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