Machine Learning Approaches for Adaptive Forecasting Systems

  • Authors

    • Dr. Yuki Nakamura Associate Professor, Kyoto University, Japan. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2024PI4R6H

    Published 04-04-2024

  • Adaptive Forecasting, Machine Learning, Predictive Analytics, Online Learning, Concept Drift, Time Series Forecasting, Neural Networks, Ensemble Learning, Forecast Accuracy, Intelligent Systems

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    Y. Nakamura, “Machine Learning Approaches for Adaptive Forecasting Systems”, IJMLPA, vol. 7, no. 1, pp. 01–16, Apr. 2024, doi: 10.67228/3142788X/IJMLPA-2024PI4R6H.

  • Abstract

    Adaptive forecasting systems have become essential for analyzing dynamic and uncertain real-world data, where traditional static forecasting models often fail due to changing patterns, concept drift, and non-stationary conditions. Machine learning techniques, including supervised learning, ensemble methods, neural networks, and hybrid models, enable continuous learning and improved prediction accuracy by adapting to new data. This study reviews machine learning-based adaptive forecasting approaches and presents a generalized framework comprising data acquisition, preprocessing, feature engineering, adaptive learning, forecasting, and performance evaluation. Special emphasis is placed on model updating, online learning, feature selection, and dynamic parameter optimization. Experimental results show that adaptive machine learning models outperform conventional forecasting methods in accuracy, responsiveness, and robustness, making them highly effective for large-scale predictive applications across finance, healthcare, energy, transportation, and industrial systems. The study highlights their potential to support next-generation intelligent forecasting and predictive analytics systems.

  • References

    [1] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ, USA: Wiley, 2015.

    [2] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, Australia: OTexts, 2021.

    [3] C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. Boca Raton, FL, USA: CRC Press, 2003.

    [4] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [5] V. Vapnik, Statistical Learning Theory. New York, NY, USA: Wiley, 1998.

    [6] C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

    [7] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [8] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.

    [9] T. G. Dietterich, “Ensemble methods in machine learning,” in Multiple Classifier Systems, Berlin, Germany: Springer, 2000, pp. 1–15.

    [10] J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Computing Surveys, vol. 46, no. 4, pp. 1–37, 2014.

    [11] A. Bifet and R. Gavaldà, “Learning from time-changing data with adaptive windowing,” in Proc. SIAM Int. Conf. Data Mining, Minneapolis, MN, USA, 2007, pp. 443–448.

    [12] M. M. Masud, J. Gao, L. Khan, J. Han, and B. Thuraisingham, “Classification and novel class detection in concept-drifting data streams,” IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 10, pp. 1467–1480, 2011.

    [13] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: Results, findings, conclusion and way forward,” International Journal of Forecasting, vol. 34, no. 4, pp. 802–808, 2018.

    [14] H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent neural networks for time series forecasting: Current status and future directions,” International Journal of Forecasting, vol. 37, no. 1, pp. 388–427, 2021.

    [15] S. F. Crone, M. Hibon, and K. Nikolopoulos, “Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction,” International Journal of Forecasting, vol. 27, no. 3, pp. 635–660, 2011.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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