Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm
is likely to perform best for my problem? Recognizing the problem as a learning task in the …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Metalearning attracted considerable interest in the machine learning community in the last
years. Yet, some disagreement remains on what does or what does not constitute a …

Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …

Retail sales forecasting often requires forecasts for thousands of products for many stores.
We present a meta-learning framework based on newly developed deep convolutional …

Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

In research of time series forecasting, a lot of uncertainty is still related to the task of
selecting an appropriate forecasting method for a problem. It is not only the individual …

Time series data appears in many real-world fields such as energy, transportation,
communication systems. Accurate modelling and forecasting of time series data can be of …

For univariate forecasting, there are various statistical models and computational algorithms
available. In real-world exercises, too many choices can create difficulties in selecting the …