Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network

Year 2024, Volume: 8 Issue: 2, 1 - 7, 13.09.2024

Emine Kölemen

https://doi.org/10.34110/forecasting.1468420

Cited By: 1

Abstract

It is seen that artificial neural networks have begun to be used extensively in the literature in solving the time series forecasting problem. In addition to artificial neural networks, classical forecasting methods can often be used to solve this problem. It is seen that classical forecasting methods give successful results for linear time series analysis. However, there is no linear relationship in many time series. Therefore, it can be thought that deep artificial neural networks, which contain more parameters but create more flexible non-linear model structures compared to classical time series forecasting methods, may enable the production of more successful forecasting methods. In this study, the problem of forecasting hazelnut export amounts according to seasons in Turkey with a dendritic neuron model artificial neural network is discussed. In this study, a training algorithm based on the particle swarm optimization algorithm is given for training the dendritic neuron model artificial neural network. The motivation of the study is to investigate Turkey's hazelnut export amounts according to seasons, using a dendritic neuron model artificial neural network. The performance of the proposed method has been compared with artificial neural networks used in the literature.

Keywords

Dendritic Neuron Model, Forecasting, Turkey Hazelnut Export.

References

• [1] Shin, Y., & Ghosh, J. (1991, July). The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation. In IJCNN-91-Seattle international joint conference on neural networks (Vol. 1, pp. 13-18). IEEE.
• [2] Yadav, R. N., Kalra, P. K., & John, J. (2007). Time series prediction with single multiplicative neuron model. Applied soft computing, 7(4), 1157-1163.
• [3] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
• [4] Todo, Y., Tamura, H., Yamashita, K., & Tang, Z. (2014). Unsupervised learnable neuron model with nonlinear interaction on dendrites. Neural Networks, 60, 96-103.
• [5] Sossa, H., & Guevara, E. (2014). Efficient training for dendrite morphological neural networks. Neurocomputing, 131, 132-142.
• [6] Yu, Y., Song, S., Zhou, T., Yachi, H., & Gao, S. (2016, December). Forecasting house price index of China using dendritic neuron model. In 2016 International Conference on Progress in Informatics and Computing (PIC) (pp. 37-41). IEEE.
• [7] Zhou, T., Gao, S., Wang, J., Chu, C., Todo, Y., & Tang, Z. (2016). Financial time series prediction using a dendritic neuron model. Knowledge-Based Systems, 105, 214-224.
• [8] Ji, J., Song, Z., Tang, Y., Jiang, T., & Gao, S. (2016, December). Training a dendritic neural model with genetic algorithm for classification problems. In 2016 International Conference on Progress in Informatics and Computing (PIC) (pp. 47-50). IEEE.
• [9] Chen, W., Sun, J., Gao, S., Cheng, J. J., Wang, J., & Todo, Y. (2017). Using a single dendritic neuron to forecast tourist arrivals to Japan. IEICE TRANSACTIONS on Information and Systems, 100(1), 190-202.
• [10] Jia, D., Zheng, S., Yang, L., Todo, Y., & Gao, S. (2018, November). A dendritic neuron model with nonlinearity validation on Istanbul stock and Taiwan futures exchange indexes prediction. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 242-246). IEEE.
• [11] Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2018). Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE transactions on neural networks and learning systems, 30(2), 601-614
• [12] Song, S., Chen, X., Tang, C., Song, S., Tang, Z., & Todo, Y. (2019). Training an approximate logic dendritic neuron model using social learning particle swarm optimization algorithm. IEEE Access, 7, 141947-141959.
• [13] Qian, X., Wang, Y., Cao, S., Todo, Y., & Gao, S. (2019). Mr2DNM: a novel mutual information-based dendritic neuron model. Computational intelligence and neuroscience, 2019.
• [14] Jia, D., Fujishita, Y., Li, C., Todo, Y., & Dai, H. (2020). Validation of large-scale classification problem in dendritic neuron model using particle antagonism mechanism. Electronics, 9(5), 792.
• [15] Zhang, T., Lv, C., Ma, F., Zhao, K., Wang, H., & O'Hare, G. M. (2020). A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform. Neurocomputing, 397, 438-446.
• [16] Song, Z., Tang, Y., Ji, J., & Todo, Y. (2020). Evaluating a dendritic neuron model for wind speed forecasting. Knowledge-Based Systems, 201, 106052.
• [17] Wang, Z., Gao, S., Wang, J., Yang, H., & Todo, Y. (2020). A dendritic neuron model with adaptive synapses trained by differential evolution algorithm. Computational intelligence and neuroscience, 2020.
• [18] Wang, S., Yu, Y., Zou, L., Li, S., Yu, H., Todo, Y., & Gao, S. (2020). A novel median dendritic neuron model for prediction. IEEE Access, 8, 192339-192351.
• [19] Yu, J., Shi, J., Li, Z., He, H., & Gao, S. (2020, December). Single dendritic neuron model trained by spherical search algorithm for classification. In 2020 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 30-33). IEEE.
• [20] Xu, W., Li, C., Dou, Y., Zhang, M., Dong, Z., Jia, D., & Ban, X. (2021, September). Optimizing the weights and thresholds in dendritic neuron model using the whale optimization algorithm. In Journal of Physics: Conference Series (Vol. 2025, No. 1, p. 012037). IOP Publishing.
• [21] He, H., Gao, S., Jin, T., Sato, S., & Zhang, X. (2021). A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction. Applied Soft Computing, 108, 107488.
• [22] Tang, C., Todo, Y., Ji, J., Lin, Q., & Tang, Z. (2021). Artificial immune system training algorithm for a dendritic neuron model. Knowledge-Based Systems, 233, 107509.
• [23] Gao, S., Zhou, M., Wang, Z., Sugiyama, D., Cheng, J., Wang, J., & Todo, Y. (2021). Fully complex-valued dendritic neuron model. IEEE transactions on neural networks and learning systems, 34(4), 2105-2118.
• [24] Yilmaz, A., & Yolcu, U. (2022). Dendritic neuron model neural network trained by modified particle swarm optimization for time‐series forecasting. Journal of Forecasting, 41(4), 793-809.
• [25] Al-Qaness, M. A., Ewees, A. A., Abualigah, L., AlRassas, A. M., Thanh, H. V., & Abd Elaziz, M. (2022). Evaluating the applications of dendritic neuron model with metaheuristic optimization algorithms for crude-oil-production forecasting. Entropy, 24(11), 1674.
• [26] Tang, Y., Song, Z., Zhu, Y., Hou, M., Tang, C., & Ji, J. (2022). Adopting a dendritic neural model for predicting stock price index movement. Expert Systems with Applications, 205, 117637.
• [27] Li, J., Liu, Z., Wang, R. L., & Gao, S. (2023). Dendritic Deep Residual Learning for COVID‐19 Prediction. IEEJ Transactions on Electrical and Electronic Engineering, 18(2), 297-299.
• [28] Yilmaz, A., & Yolcu, U. (2023). A robust training of dendritic neuron model neural network for time series prediction. Neural Computing and Applications, 35(14), 10387-10406.
• [29] Gul, H. H., Egrioglu, E., & Bas, E. (2023). Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm. Information Sciences, 629, 398-412.
• [30] Olmez, E., Egrioglu, E., & Bas, E. (2023). Bootstrapped dendritic neuron model artificial neural network for forecasting. Granular Computing, 8(6), 1689-1699.
• [31] Egrioglu, E., Bas, E., & Karahasan, O. (2023). Winsorized dendritic neuron model artificial neural network and a robust training algorithm with Tukey’s biweight loss function based on particle swarm optimization. Granular Computing, 8(3), 491-501.
• [32] Zhang, Y., Yang, Y., Li, X., Yuan, Z., Todo, Y., & Yang, H. (2023). A dendritic neuron model optimized by meta-heuristics with a power-law-distributed population interaction network for financial time-series forecasting. Mathematics, 11(5), 1251.
• [33] Ding, Y., Yu, J., Gu, C., Gao, S., & Zhang, C. (2024). A multi-in and multi-out dendritic neuron model and its optimization. Knowledge-Based Systems, 286, 111442.
• [34] Bas, E., Egrioglu, E., & Cansu, T. (2024). Robust training of median dendritic artificial neural networks for time series forecasting. Expert Systems with Applications, 238, 122080.