Abstract
Many energy load forecasting models have been established from batch-based supervised learning models where the whole data must be loaded to learn. Due to the sheer volumes of the accumulated consumption data which arrive in the form of continuous data streams, such batch-mode learning requires a very long time to rebuild the model. Incremental learning, on the other hand, is an alternative for online learning and prediction which learns the data stream in segments. However, it is known that its prediction performance falls short when compared to batch learning. In this paper, we propose a novel approach called Shadow Features (SF) which offer extra dimensions of information about the data streams. SF are relatively easy to compute, suitable for lightweight online stream mining.
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Getty Museum, J.P.: Photography: Discovery and Invention. ISBN 0-89236-177-8 (1990)
Vishwakarma, D.K., Rawat, P., Kapoor, R.: Human activity recognition using gabor wavelet transform and ridgelet transform. In: 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015), vol. 57, pp. 630–636 (2015)
Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International Conference on Body Area Networks, pp. 92–98 (2011)
Fong, S.: Adaptive forecasting of earthquake time series by incremental decision tree algorithm. Inf. J. 16(12), 8387–8395 (2013). International Information Institute (Tokyo)
Witt, A., Malamud, B.D.: Quantification of long-range persistence in geophysical time series: conventional and benchmark-based improvement techniques. Surv. Geophys. (Springer) 34(5), 541–651 (2013)
Zhou, N.: Earthquake Forecasting Using Dynamic Hurst Coefficiency, MSc thesis, Department of Computer and Information Science, University of Macau, Macau SAR (2013)
Holmes, B.A.: Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. In: MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. Workshop and Conference Proceedings. vol. 11: Workshop on Applications of Pattern Analysis, pp. 1–14 (2010)
Frank, E., Pfahringer, B.: Propositionalisation of multi-instance data using random forests. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS (LNAI), vol. 8272, pp. 362–373. Springer, Heidelberg (2013). doi:10.1007/978-3-319-03680-9_37
Acknowledgement
The authors are thankful for the financial support from the Research Grant Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF), Grant no. MYRG2015-00128-FST, offered by the University of Macau, FST, and RDAO.
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Fong, S., Yuen, M., Wong, R.K., Song, W., Cho, K. (2016). Real-Time Stream Mining Electric Power Consumption Data Using Hoeffding Tree with Shadow Features. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_56
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DOI: https://doi.org/10.1007/978-3-319-49586-6_56
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