{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T03:50:28Z","timestamp":1776829828515,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe research and innovation program","award":["101138678"],"award-info":[{"award-number":["101138678"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The importance of enhancing the accuracy of time-series forecasting using artificial intelligence tools is increasingly critical in light of the rapid advancements in modern technologies, particularly deep learning and neural networks. These approaches have already shown considerable advantages over traditional methods, especially due to their capacity to efficiently process large datasets and detect complex patterns. A crucial step in the forecasting process is the preprocessing of time-series data, which can greatly improve the training quality of neural networks and the precision of their predictions. This paper introduces a novel preprocessing technique that integrates information from both the time and frequency domains. To achieve this, the authors developed a feature extraction\u2013extension scheme, where the extraction component focuses on obtaining the phase and amplitude of complex numbers through fast Fourier transform (FFT) and the extension component expands the time intervals by enriching them with the corresponding frequency characteristics of each individual time point. Building upon this preprocessing method, the FFT-LSTM forecasting model, which combines the strengths of FFT and Long Short-Term Memory (LSTM) recurrent neural networks, was enhanced. The simulation of the improved FFT-LSTM model was carried out on two time series with distinct characteristics. The results revealed a substantial improvement in forecasting accuracy compared to established methods in this domain, with about a 5% improvement in MAE and RMSE, thereby validating the effectiveness of the proposed approach for forecasting applications across various fields.<\/jats:p>","DOI":"10.3390\/bdcc9020035","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T06:43:07Z","timestamp":1739169787000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction\u2013Extension Scheme"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5157-9118","authenticated-orcid":false,"given":"Kyrylo","family":"Yemets","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-0096","authenticated-orcid":false,"given":"Ivan","family":"Izonin","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"},{"name":"Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-2584","authenticated-orcid":false,"given":"Ivanna","family":"Dronyuk","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, Jan Dlugosz University in Czestochowa, 42-200 Czestochowa, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Casolaro, A., Capone, V., Iannuzzo, G., and Camastra, F. (2023). Deep Learning for Time Series Forecasting: Advances and Open Problems. Information, 14.","DOI":"10.3390\/info14110598"},{"key":"ref_2","first-page":"307","article-title":"Investigation of Artificial Intelligence Methods in the Short-Term and Middle-Term Forecasting in Financial Sphere","volume":"Volume 1107","author":"Zgurovsky","year":"2023","journal-title":"System Analysis and Artificial Intelligence"},{"key":"ref_3","first-page":"13","article-title":"Investigation of Hybrid Neo-Fuzzy Neural Networks in the Problem of Pandemic Forecasting","volume":"3018","author":"Zaychenko","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_4","first-page":"1","article-title":"Investigation of Hybrid Deep Learning Networks in Forecasting Energy Supply","volume":"3538","author":"Zaychenko","year":"2023","journal-title":"CEUR Workshop Proc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zaychenko, Y., Zaichenko, H., and Kuzmenko, O. (2023). Investigation of Computational Intelligence Methods in Forecasting at Financial Markets. Syst. Res. Inf. Technol., 54\u201365.","DOI":"10.20535\/SRIT.2308-8893.2023.3.04"},{"key":"ref_6","first-page":"6","article-title":"Devising a Method for Constructing the Optimal Model of Time Series Forecasting Based on the Principles of Competition","volume":"5","author":"Mulesa","year":"2021","journal-title":"East.-Eur. J. Enterp. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tokg\u00f6z, A., and \u00dcnal, G. (2018, January 2\u20135). A RNN Based Time Series Approach for Forecasting Turkish Electricity Load. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404313"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sadon, A.N., Ismail, S., Jafri, N.S., and Shaharudin, S.M. (2021, January 8\u20139). Long Short-Term vs Gated Recurrent Unit Recurrent Neural Network for Google Stock Price Prediction. Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS), Ipoh, Malaysia.","DOI":"10.1109\/AiDAS53897.2021.9574312"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Geche, F., Mitsa, O., Mulesa, O., and Horvat, P. (2022, January 4\u20137). Synthesis of a Two Cascade Neural Network for Time Series Forecasting. Proceedings of the 2022 IEEE 3rd International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine.","DOI":"10.1109\/SAIC57818.2022.9922991"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Geche, F., Batyuk, A., Mulesa, O., and Voloshchuk, V. (2020, January 21\u201325). The Combined Time Series Forecasting Model. Proceedings of the 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP47368.2020.9204311"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Usmani, M., Memon, Z.A., Zulfiqar, A., and Qureshi, R. (2024). Preptimize: Automation of Time Series Data Preprocessing and Forecasting. Algorithms, 17.","DOI":"10.3390\/a17080332"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, J., and Yang, Z. (2024). Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms. Appl. Sci., 14.","DOI":"10.3390\/app14198922"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fatima, S.S.W., and Rahimi, A. (2024). A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems. Machines, 12.","DOI":"10.3390\/machines12060380"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Agrawal, S., and Sharma, D.K. (2022, January 23\u201325). Feature Extraction and Selection Techniques for Time Series Data Classification: A Comparative Analysis. Proceedings of the 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.","DOI":"10.23919\/INDIACom54597.2022.9763125"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, W.-J., Lo, S.-H., Young, H.-T., and Hung, C.-L. (2019). Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis. Appl. Sci., 9.","DOI":"10.3390\/app9071462"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yemets, K., and Gregus, M. (2025). A Transformer-Based Time Series Forecasting Model with an Efficient Data Preprocessing Scheme for Enhancing Wind Farm Energy Production. Bull. Electr. Eng. Inform., 25.","DOI":"10.3390\/s25030652"},{"key":"ref_17","first-page":"696","article-title":"An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks","volume":"Volume 77","author":"Babichev","year":"2022","journal-title":"Lecture Notes in Computational Intelligence and Decision Making"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2377510","DOI":"10.1080\/08839514.2024.2377510","article-title":"Generalized Performance of LSTM in Time-Series Forecasting","volume":"38","author":"Prater","year":"2024","journal-title":"Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"122333","DOI":"10.1016\/j.eswa.2023.122333","article-title":"NOA-LSTM: An Efficient LSTM Cell Architecture for Time Series Forecasting","volume":"238","author":"Yadav","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Siami Namin, A. (2018, January 17\u201320). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","article-title":"A Survey on Long Short-Term Memory Networks for Time Series Prediction","volume":"99","author":"Lindemann","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lotfi Hachemi, M., Ghomari, A., Hadjadj-Aoul, Y., and Rubino, G. (2021, January 7\u201310). Mobile Traffic Forecasting Using a Combined FFT\/LSTM Strategy in SDN Networks. Proceedings of the 2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR), Paris, France.","DOI":"10.1109\/HPSR52026.2021.9481863"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hounkpe Houenou, A.G., Nounangnonhou, C.T., Agbokpanzo, R.G., Didavi, K.B.A., Sedjro, J., and Agbomahena, B.M. (2024, January 30\u201331). Hybrid FFT-Hyperband-LSTM Model for Direct Short-Term PV Power Forecasting. Proceedings of the 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Bandung, Indonesia.","DOI":"10.1109\/ICSIMA62563.2024.10675582"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., and Zheng, M. (2019). Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform. Appl. Sci., 9.","DOI":"10.3390\/app9061108"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"012003","DOI":"10.1088\/1742-6596\/2846\/1\/012003","article-title":"CEEMD-FFT-BiLSTM Based Model for Power Line Loss Prediction","volume":"2846","author":"Zhao","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_26","unstructured":"Godahewa, R., Bergmeir, C., Webb, G., Abolghasemi, M., Hyndman, R., and Montero-Manso, P. (2024, May 20). Wind Power Dataset (4 Seconds Observations) 2020. Available online: https:\/\/zenodo.org\/records\/4656032."},{"key":"ref_27","unstructured":"(2024, November 21). Sunspot Number Version 2.0: New Data and Conventions|SIDC. Available online: https:\/\/www.sidc.be\/SILSO\/newdataset."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/2\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:29:43Z","timestamp":1760027383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/2\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,8]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["bdcc9020035"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9020035","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,8]]}}}