Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

Boosting Algorithm (BA) is state-of-the-art in major competitions, especially in the M4 and
M5 time series forecasting competitions. However, the use of BA requires tedious feature …

In engineering design optimization problems, the optimal solution can improve the design
quality of complex engineering system and reduce a lot of cost consumption, so it is of great …

Modeling complex hierarchical and grouped feature interaction in the multivariate time
series data is indispensable to comprehend the data dynamics and predicting the future …

The fuzzy time series (FTS) model is widely used to forecast time series data. However, the
predicted results of FTS are poor for industrial time series data, especially when data …

Asset prices play a significant role in the financial survival and profitability of ship-owning
firms. In a highly volatile shipping market, prices of newbuilding ships must be predicted to …

Time charter rates must be predicted accurately to assist sensible decisions in the global,
highly volatile shipping market. Time charter rates are affected by multiple factors, such as …

Fuzzy time series is a dynamic process in time series forecasting due to which it has gained
a lot of attention from researchers. In this process, prediction accuracy is influenced by …

This paper provides evidence on the use of Random Regression Forests (RRF) for optimal
lag selection. Using an extended sample of 144 data series, of various data types with …

Estimating second-hand ship prices in the highly uncertain and cyclical ship trading market
is a challenge due to its volatile nature. In this study, we propose a novel and highly …