Browse > Article
http://dx.doi.org/10.7469/JKSQM.2022.50.4.701

Forecasting Baltic Dry Index by Implementing Time-Series Decomposition and Data Augmentation Techniques  

Han, Min Soo (Department of Shipping Management, Graduate School of Korea Maritime and Ocean University)
Yu, Song Jin (Department of Shipping Management and Economics, Korea Maritime and Ocean University)
Publication Information
Journal of Korean Society for Quality Management / v.50, no.4, 2022 , pp. 701-716 More about this Journal
Abstract
Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.
Keywords
Time-Series Decomposition; Data Augmentation; Forecasting Baltic Dry Index;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Abdollahi, H. 2020. A novel hybrid model for forecasting crude oil price based on time series decomposition. Applied energy, 267, 115035.
2 Allaire, JJ. & Chollet, F. 2022. Package 'keras'. R Interface to 'Keras'.
3 Allaire, JJ. & Tang, Y. 2022. Package 'tensorflow'. R Interface to 'TensorFlow'.
4 Benkachcha, S., Benhra, J., & El Hassani, H. 2015. Seasonal time series forecasting models based on artificial neural network. International Journal of Computer Applications 116(20).
5 Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. 1990. STL: A seasonal-trend decomposition. J. Off. Stat 6(1):3-73.
6 Demir, S., Mincev, K., Kok, K., & Paterakis, N. G. 2021. Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting. Applied Energy 304: 117695.
7 Fritsch, S., Guenther, F., & Guenther, M. F. 2019. Package 'neuralnet'. Training of Neural Networks.
8 Gers, F. A., Eck, D., & Schmidhuber, J. 2002. Applyting LSTM to time series predictable through time-window approaches. In Neural Nets WIRN Vietri-01, pp. 193-200. Springer, London.
9 Hafen, R. 2016. Package 'stlplus'. Ehanced Seasonal Decomposition of Time Series by Loess.
10 HAN, M. & Yu, S. J. 2019. Prediction of Baltic Dry Index by Applications of Long Short-Term Memory. Journal of the Korean Society for Quality Management 47(3):497-508.
11 Hansen, J. V. & Nelson, R. D. 2003. Forecasting and recombining time-series components by using neural networks. Journal of the Operational Research Society 54(3):307-317.   DOI
12 Iwana, B. K. & Uchida, S. 2021. An empirical survey of data augmentation for time series classification with neural networks. Plos one 16(7):e0254841.
13 Kang, S., Cho, K., & Na, M. 2021. Forecasting Crop Yield Using Encoder-Decoder Model with Attention. Journal of the Korean Society for Quality Management 49(4):569-579.
14 Mendez-Jimenez, I., & Cardenas-Montes, M. 2018.. Time series decomposition for improving the forecasting performance of convolutional neural networks. In Conference of the Spanish Association for Artificial Intelligence (pp. 87-97). Springer, Cham.
15 Khandelwal, I., Adhikari, R., & Verma, G. 2015. Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Procedia Computer Science 48:173-179.   DOI
16 Lee, S. W. & Kim, H. Y. 2020. Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation. Expert Systems with Applications 161:113704.
17 Lin, Y., Koprinska, I., & Rana, M. 2021. SSDNet: State space decomposition neural network for time series forecasting. In 2021 IEEE International Conference on Data Mining (ICDM), pp. 370-378. IEEE.
18 Oh, C., Han, S. & Jeong, J. 2020. Time-series data augmentation based on interpolation. Procedia Computer Science 175:64-71.   DOI
19 Oliveira, D. D., Rampinelli, M., Tozatoo, G. Z., Andreao, R. V., & Muller, S. M. 2021. Forecasting vehicular traffic flow using MLP and LSTM. Neural Computing and applications 33(24):17245-17256.   DOI
20 Ouyang, Z., Ravier, P., & Jabloun, M. 2021. STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones. Engineering Proceedings 5(1):42.
21 Quast, B. & Fichou, D. (2022). Package 'rnn'. Recurrent Nerual Network.
22 Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478.
23 Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159-175.   DOI