Abstract
Feature engineering is a decisive step in time series forecasting, as it directly influences the performance of predictive models. In recent years, the Fast Fourier Transform (FFT) has gained popularity as an algorithm for extracting frequency-domain features from time series data. In this paper, we investigate the potential of using FFT as feature engineering to improve the accuracy and efficiency of time-series forecasting models. We performed a comparative analysis of the performance of models trained with FFT-based features versus traditional time domain features on two datasets. Our results demonstrate that FFT-based feature engineering outperforms traditional feature engineering methods in terms of forecast accuracy and computational efficiency. Additionally, we provide insights into the interpretability of the frequency domain features and their relationship with the underlying time series patterns. Overall, our study suggests that FFT-based feature engineering is a promising approach to enhance the performance of time-series forecasting models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Matuozzo, A., Yoo, P.D., Provetti, A.: A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions. Expert Syst. Appl. 223, 119854 (2023)
Zhou, Z., Zhao, Z., Zhang, X., Zhang, X., Jiao, P.: Improvement of accuracy and resilience in fhr classification via double trend accumulation encoding and attention mechanism. Biomed. Signal Process. Control 85, 104929 (2023)
Zhang, Z., Wang, J., Wei, D., Xia, Y.: An improved temporal convolutional network with attention mechanism for photovoltaic generation forecasting. Eng. Appl. Artif. Intell. 123, 106273 (2023)
Wang, J., Li, Z.: Wind speed interval prediction based on multidimensional time series of convolutional neural networks. Eng. Appl. Artif. Intell. 121, 105987 (2023)
Qin, Y., Luo, H., Zhao, F., Fang, Y., Tao, X., Wang, C.: Spatio-temporal hierarchical mlp network for traffic forecasting. Inf. Sci. 632, 543–554 (2023)
Zhang, X., Kim, T.: A hybrid attention and time series network for enterprise sales forecasting under digital management and edge computing. J. Cloud Comput. 12(1), 1–21 (2023)
Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex fourier series. Math. Comput. 19(90), 249–259 (1965)
Välimäki, V., Bilbao, S.: Giant ffts for sample-rate conversion. AES J. Audio Eng. Soc. 71(3), 88–99 (2023)
Liu, Z., Yan, L., Liu, Y., Ruan, X.: Two dimension-reduction probabilistic models for simulating nonstationary turbulent wind fields. Probabil. Eng. Mech. 72, 103435 (2023)
Szymkowski, M., Jasiński, P., Saeed, K.: Iris-based human identity recognition with machine learning methods and discrete fast fourier transform. Innov. Syst. Softw. Eng. 17(3), 309–317 (2021)
Han, J.H., et al.: Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy 53, 658–665 (2018)
Fatimah, B., Singhal, A., Singh, P.: A multi-modal assessment of sleep stages using adaptive fourier decomposition and machine learning. Comput. Biol. Med. 148, 105877 (2022)
Al-Sharu, W.N., Member, A.M.A., Qazan, S., Alqudah, A.: Detection of valvular heart diseases using fourier transform and simple cnn model. IAENG Int. J. Comput. Sci. 49(4), 985–993 (2022)
Shih, C.-H., Lin, C.-J., Lee, C.-L.: Integrated image sensor and deep learning network for fabric pilling classification. Sens. Mater. 34(1), 93–104 (2022)
Han, B., Yang, X., Ren, Y., Lan, W.: Comparisons of different deep learning-based methods on fault diagnosis for geared system. Int. J. Distrib. Sensor Netw. 15(11) (2019)
Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference, vol. 35, pp. 11106–11115. AAAI Press (2021)
Wang, X., Liu, H., Junzhao, D., Yang, Z., Dong, X.: Clformer: locally grouped auto-correlation and convolutional transformer for long-term multivariate time series forecasting. Eng. Appl. Artif. Intell. 121, 106042 (2023)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 1–11 (2017)
Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? (2022). arXiv preprint arXiv:2205.13504
Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: Stl: a seasonal-trend decomposition. J. Off. Stat 6(1), 3–73 (1990)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
de O Santos Júnior, D.S., de Oliveira, J.F.L., de Mattos Neto, P.S.G.: An intelligent hybridization of arima with machine learning models for time series forecasting. Knowl.-Based Syst. 175, 72–86 (2019)
Dudek, G.: Multilayer perceptron for short-term load forecasting: from global to local approach. Neural Comput. Appl. 32(8), 3695–3707 (2020)
O’Shea, K., Nash, R.: An introduction to convolutional neural networks (2015). arXiv preprint arXiv:1511.08458
Tripathi, M.: Analysis of convolutional neural network based image classification techniques. J. Innov. Image Process. (JIIP) 3(02), 100–117 (2021)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018). arXiv preprint arXiv:1803.01271
Vega, B., Nepomuceno-Chamorro, I., Rubio-Escudero, C., Riquelme, J.: Ocean: ordinal classification with an ensemble approach. Inf. Sci. 580, 08 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Galán-Sales, F.J., Reina-Jiménez , P., Carranza-García, M., Luna-Romera, J.M. (2023). An Approach to Enhance Time Series Forecasting by Fast Fourier Transform. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-42529-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42528-8
Online ISBN: 978-3-031-42529-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)