Forecasting Dry Bulk Freight Index with Improved SVM

Abstract

An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

“…Duru et al [18] propose a fuzzy-DELPHI adjustment method of increasing accuracy when statistically forecasting dry bulk shipping indices. Han et al [19] use wavelet transform to denoise the BDI data series and combine wavelet transform and a support vector machine to forecast BDI. Zeng et al [20] use empirical mode decomposition (EMD) and artificial neural networks (ANN) to forecast the BDI.…”

Forecasting of Dry Freight Index Data by Using Machine Learning Algorithms

“…According to their studies, the ANN is a considerable method for modelling and forecasting of BDI. Han et al [2] presented the improved support vector machine model which is combined model of wavelet transform and support vector machine in order to forecast dry bulk freight index. Wong [4] introduced that Autoregressive integrated moving average fits better than Fuzzy heuristic model for the prediction of BDI in their studies.…”

“…It is generally hard to forecast these indexes because they are volatile, complex, and cyclic [1]. The prediction of the trend of dry bulk market becomes difficult since the affecting factors of price of dry bulk market are complexities [2].…”

Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine

“…Fan et al analyzed the statistical data of the Baltic Capesize Freight Index (BCI) and the daily return rate sequences to improve forecast reliability of the international dry bulk shipping market [4]. Han et al adopted wavelet transform to denoise the BDI data series and developed a combined model of wavelet transform and support vector machine to forecast BDI [5]. Zeng et al proposed a method based on empirical mode decomposition (EMD) and artificial neural networks (ANN) is developed for Baltic Dry Index (BDI) forecasting [26].…”