Accurate forecasts are the base for correct decisions in revenue management. This paper
addresses two novel neural network structures for short-term railway passenger demand …

A time series is a sequence of observations collected over fixed sampling intervals. Several
real-world dynamic processes can be modeled as a time series, such as stock price …

We present a novel framework that applies a meta-learning approach to clustering
algorithms. Given a dataset, our meta-learning approach provides a ranking for the …

A statistical approach, based on full range Gaussian Markov random field model, is
proposed for texture analysis such as texture characterization, unique representation …

Accurate forecasts are essential for allocating perishable resources. As arrivals are the
accumulation of reservations, utilizing reservation records to construct forecasting models is …

In this work, we proposed the use of Support Vector Machines (SVM) to predict the
performance of machine learning algorithms based on features of the learning problems …

Meta-learning techniques can be very useful for supporting non-expert users in the
algorithm selection task. In this work, we investigate the use of different components in an …

In this work, we present the use of Ranking Meta-Learning approaches to ranking and
selecting algorithms for problems of time series forecasting and clustering of gene …

Demand forecasting is an essential process for any firm whether it is a supplier,
manufacturer or retailer. A large number of research works about time series forecast …

Meta-Learning has been successfully applied to acquire knowledge used to support the
selection of learning algorithms. Each training example in Meta-Learning (ie each meta …