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Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challenges when accurately predicting future forecasts. This paper proposes a novel Recurrent ensemble deep Random Vector Functional Link (RedRVFL) network for financial time series forecasting. The proposed model leverages randomly initialized and fixed weights for the recurrent hidden layers, ensuring stability during training. Furthermore, incorporating stacked hidden layers enables deep representation learning, facilitating the extraction of complex patterns from the data. The proposed model generates the forecast by combining the outputs of each layer through an ensemble approach. A comparative analysis was conducted against several state-of-the-art models over financial time-series datasets, and the results demonstrated the superior performance of our proposed model in terms of forecasting accuracy and predictive capability. [ABSTRACT FROM AUTHOR]

• A novel PIMSE approach is proposed to forecast second-hand oil tanker price. • The proposed algorithm ensures a parsimonious lag structure while maintaining high interpretability of the selected lags and explanatory variables. • The proposed model outperforms the baselines in terms of three error metrics on three time series from the shipping market. 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 interpretable model termed as the parsimonious intelligent model search engine (PIMSE), to investigate the relationship between key supply variables and the prices of second-hand oil tankers. Through empirical evaluation using a time series dataset spanning from 2002 to 2020, encompassing three types of oil tankers (VLCC, Suezmax, and Aframax), we assess the effectiveness and performance of PIMSE. The results demonstrate the superior performance of PIMSE compared to other estimation models in terms of its accuracy and stability. It can effectively address abrupt structural change and capture trend and seasonal variations in the time series data. Moreover, the high interpretability of PIMSE provides valuable insights into the factors that influence second-hand ship prices, empowering stakeholders in the shipping industry to make well-informed investment decisions. By leveraging PIMSE, decision-makers can gain a deeper understanding of market dynamics and navigate the complexities of the ship trading industry. Notably, PIMSE's ability to handle the cyclical nature of the ship trading market and incorporate trend and seasonal variations enhances the robustness and accuracy of second-hand ship price estimation. [ABSTRACT FROM AUTHOR]

The reliable control of wave energy devices highly relies on the forecasts of wave heights. However, the dynamic characteristics and significant fluctuation of waves' historical data pose challenges to precise predictions. Neural networks offer a promising solution to forecast the wave heights by extracting meaningful features from historical observations. This paper proposes a novel hybrid random vector functional link network with the ensemble and deep learning benefits. Hierarchical stacks of hidden layers are constructed to enforce the deep representations of the time series. Individual output layers follow all enhancement layers to adopt ensemble learning. A neuron pruning strategy is proposed to remove the noisy information from the random features and boost the network's performance. Besides, the proposed network is further utilized to forecast the additive and multiplicative residuals from the ARIMA method. Finally, the ensemble of additive-ARIMA-edRVFL, multiplicative-ARIMA-edRVFL, and edRVFL achieves the best average rankings around two for three forecasting horizons. The proposed ensemble achieves an average ranking of 1.33 on four-hours ahead of forecasting in terms of root mean square error and mean absolute scaled error. Extensive experiments are conducted on twelve time series of the significant wave height. The comparative results demonstrate the superiority of the proposed model over other state-of-the-art methods. The source codes are available on https://github.com/P-N-Suganthan/CODES. [ABSTRACT FROM AUTHOR]

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 second-hand ship prices, order book, Libor interest rate, etc. However, not all factors convey predictive features to anticipate the future of time charter rates. Therefore, extracting predictive features from multiple driving time series from the shipping market is crucial for forecasting purposes. Accordingly, this paper proposes a novel convolutional recurrent neural network for time charter rates forecasting under the multi-variate phenomenon. The proposed network first extracts features from the monthly time series using a novel convolutional filter, the annual dilated filter. The annual dilated convolutional filter can extract the predictive features effectively and impose a sparse input structure to prevent overfitting. Then, a recurrent neural network learns the temporal information from the convoluted features. An extensive comparison study with many baseline models, including the persistence (Naïve I), statistical models, and the state-of-art networks, is conducted on the time charter rates of six kinds of ships. The empirical results demonstrate the proposed model's superiority in forecasting the time charter rates. • A novel dilated convolution LSTM neural network is proposed to forecast time charter rates. • The proposed convolutional filters can extract temporal features of monthly charter rates efficiently. • The proposed model outperforms the baselines in terms of three error metrics. [ABSTRACT FROM AUTHOR]

The challenge of accurately forecasting a time series covers numerous disciplines, from economics to engineering. Among the thousands of machine learning models, random vector functional link (RVFL) is a robust and efficient model which has demonstrated its success in various challenging forecasting problems. RVFL is an efficient universal function appropriator that randomly generates the weights between the input and hidden layers. However, RVFL still lacks the strong ability to extract meaningful multi-scale features from input data because of the single-layer random mapping of enhancement nodes. Therefore, we propose to combine the empirical wavelet transformation (EWT) with RVFL to strengthen the multi-scale feature extraction ability. The EWT can decompose the original time series into several sub-series which carry the information of different frequencies. Besides, we propose a walk-forward decomposition mechanism to implement the EWT. By introducing such a walk-forward mechanism and the combination of EWT and RVFL, the hybrid model achieves high accuracy and averts the data leakage problem during forecasting. A detailed and comprehensive empirical study on twenty-six public time series validates the proposed model's superiority compared with ten popular baseline models from the literature. • A causal signal decomposition method is proposed to avert the data leakage problem. • The empirical wavelet transformation is applied to decompose the time series. • A novel hybrid RVFL network is proposed for time series forecasting. [ABSTRACT FROM AUTHOR]

Echo state network (ESN) is a reservoir computing structure consisting randomly generated hidden layer which enables a rapid learning and extrapolation process. On the other hand, the determination of inputs is still an outstanding question similar to other neural networks. Instead of utilizing the original historical data, wavelet transformations can be used to extract and reflect features with predictability (periodicity) and eliminate random oscillations (i.e. noise) which would distort the underlying predictive structure. In this regard, we propose a two-stage predictive algorithm in which the empirical wavelet transformation (EWT) has been implemented to transform the data, and ESN is utilized to execute the overall predictive process. The proposed method has been validated by twelve public datasets with different mean-volatility features. Out of sample forecasts have been compared to the baseline persistence model and conventional benchmarks. The empirical study demonstrates the superiority of the EWT–ESN model compared with other models. • A novel EWT–ESN is proposed for time series forecasting. • The EWT is developed to decompose the original time series. • A rapid predictive process has been developed with ESN structure. • Twelve datasets are used to validate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps have achieved significant success in time series modeling and forecasting. However, fuzzy cognitive maps still contain weakness to handle the nonstationarity and outliers. We propose a novel time series forecasting model based on fuzzy cognitive maps and empirical wavelet transformation in this paper. The empirical wavelet transformation is applied to decompose the original time series into different levels which capture information of different frequencies. Then, the high-order fuzzy cognitive map is trained to model the relationships among all the sub-series generated and original time series. To enhance the robustness of high-order fuzzy cognitive maps against outliers, a novel learning method based on support vector regression is designed. Finally, we divide the summation of each concept value of the high-order fuzzy cognitive map by two to obtain the numerical predictions. A comprehensive empirical study on eight public time series validates the superiority of proposed model compared with the popular baseline models from the literature. • A novel EWT-FCM is proposed to time series forecasting. • The EWT is developed to decompose the original time series. • A robust learning algorithm is designed for high-order FCM. • Proposed model is validated through an empirical study of various time series. [ABSTRACT FROM AUTHOR]