Time Series Forecasting with Data Transform and Its Application in Sport

• Autorzy: Do Hao , Chau Duc , Cai Thuc , Lam Han , Nguyen Dat , Lam Tuong , Tran Son

Identyfikatory

DOI

10.15439/2021R06

• Konferencja: Sixth International Conference on Research in Intelligent and Computing
• Języki publikacji: EN

Abstrakty

EN

Forecasting time series data is an exciting challenge. Although being complex, this is a high potential for industrial use. One of the most significant gaps in the forecasting process is the quality of data representation, especially with the time-series data. This paper proposes an effective method using an integral transform that can show hidden information of the time series data. The integral transform exploits data as a composition of many basic functions and then use this set to present the data. Mathematically, this transform converts the data into another space with another feature, showing many properties hidden in the original form. The experimental result demonstrates our suggestion can learn the transformation rules and then can be applied for many applications.

Słowa kluczowe

EN

time series forecasting; integral transform; periodic dat

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 27
• Strony: 29--32
• Opis fizyczny: Bibliogr. 10 poz., tab., wykr.

Twórcy

autor

Do Hao

• University of Science, HCM City, Vietnam
• Vietnam National University, HCM City, Vietnam

autor

Chau Duc

• University of Science, HCM City, Vietnam
• Vietnam National University, HCM City, Vietnam

autor

Cai Thuc

• OLLI Technology JSC, HCM City, Vietnam

autor

Lam Han

• Vietnam National University, HCM City, Vietnam

autor

Nguyen Dat

• OLLI Technology JSC, HCM City, Vietnam

autor

Lam Tuong

• OLLI Technology JSC, HCM City, Vietnam

autor

Tran Son

• University of Science, HCM City, Vietnam
• Vietnam National University, HCM City, Vietnam

Bibliografia

• 1. C. R. Madhuri, M. Chinta and V. V. N. V. P. Kumar, “Stock Market Prediction for Time-series Forecasting using Prophet upon ARIMA.” In: 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, http://dx.doi.org/10.1109/ICSSS49621.2020.9202042, pp. 1-5, 2020.
• 2. G. Liu, F. Xiao, C. -T. Lin and Z. Cao, “A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation.” In: IEEE Transactions on Fuzzy Systems. 28(11), http://dx.doi.org/10.1109/TFUZZ.2020.2972823, pp. 2677-2690, 2020.
• 3. S. G. N and G. S. Sheshadri, “Electrical Load Forecasting Using Time Series Analysis.” In: 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), Vijiyapur, India, http://dx.doi.org/10.1109/B-HTC50970.2020.9297986, pp. 1-6, 2020.
• 4. Ming-Che Lee, Jia-Wei Chang, Jason C. Hung, and Bae-Ling Chen, ”Exploring the Effectiveness of Deep Neural Networks with Technical Analysis Applied to Stock Market Prediction.” In: Computer Science and Information Systems, Vol. 18, No. 2, 401–418, 2021, https://doi.org/10.2298/CSIS200301002L
• 5. Radojičić, D., Radojičić, N., Kredatus, S., “A multicriteria optimization approach for the stock market feature selection.” In: Computer Science and Information Systems, Vol. 18, No. 3, 749–769, 2021, https://doi.org/doi.org/10.2298/CSIS200326044R
• 6. N. D. Hieu, N. Cat Ho and V. N. Lan, “An efficient fuzzy time series forecasting model based on quantifying semantics of words.” In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh, Vietnam, http://dx.doi.org/10.1109/RIVF48685.2020.9140755, pp. 1-6, 2020.
• 7. Brian Davies, “Integral Transforms and Their Applications.” Springer-Verlag, New York, ISBN 978-1-441-92950-1, 2002.
• 8. Lokenath Debnath and Dambaru Bhatta, “Integral Transforms and Their Applications.” Chapman and Hall/CRC, ISBN ISBN 978-1-584-88575-7, 2016.
• 9. Smith Steven, “Chapter 8: The Discrete Fourier Transform.” The Scientist and Engineer’s Guide to Digital Signal Processing (Second ed.), California Technical Publishing, ISBN 978-0-9660176-3-2, 1999.
• 10. Thomas H. Cormen, Leiserson Charles, Ronald L. Rivest, Clifford Stein, ”Chapter 30: Polynomials and the FFT.” Introduction to Algorithms (Second ed.), MIT Press and McGraw-Hill, pp. 822–848, ISBN 978-0-262-03293-3, 2001.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).