The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD
can be predicted. The price data is sourced from the Bitcoin Price Index. The task is …

Foreign exchange rate prediction is an important problem in finance and it attracts many
researchers owing to its complex nature and practical applications. Even though this …

Cryptocurrencies, with Bitcoin being the most notable example, have attracted considerable
attention in recent years, and they have experienced large fluctuations in their price. While a …

Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Both theoretical and empirical findings …

Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximators that can be applied to a wide range of time series forecasting problems with a …

Conventional methods are less robust in terms of accurately forecasting non-stationary and
nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based …

This chapter advances a hybrid forecasting model for the carbon market. The technology is
based on Least Squares Support Vector Machines augmented by particle swarm …

With the continuous growth of the global natural gas trade, the accurate prediction of natural
gas prices has become one of the most critical issues in the planning and operation of public …

In this study, an empirical mode decomposition (EMD) based neural network ensemble
learning paradigm is proposed for world crude oil spot price forecasting. For this purpose …