Abstract
Forecasting is an important analysis technique used in many application domains such as electricity management, sales and retail and, traffic predictions. The employed statistical models already provide very accurate predictions, but recent developments in these domains pose new requirements on the calculation speed of the forecast models. Especially, the often used multi-equation models tend to be very complex and their estimation is very time consuming. To still allow the use of these highly accurate forecast models, it is necessary to improve the data processing capabilities of the involved data management systems. For this purpose, we introduce a partitioning approach for multi-equation forecast models that considers the specific data access pattern of these models to optimize the data storage and memory access. With the help of our approach we avoid the redundant reading of unnecessary values and improve the utilization of the CPU cache. Furthermore, we utilize the capabilities of modern multi-core hardware and parallelize the model estimation. Our experimental results on real-world data show speedups of up to 73x for the initial model estimation. Thus, our partitioning and parallelization approach significantly increases the efficiency of multi-equation models.
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References
MIRABEL Project (2011), http://www.mirabel-project.eu
MeRegio Project (2011), http://www.meregio.de/en/
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. John Wiley & Sons Inc. (1970)
Winters, P.R.: Forecasting sales by exponentially weighted moving averages. Management Science, 324–342 (April 1960)
Bunnoon, P., Chalermyanont, K., Limsakul, C.: A computing model of artificial intelligent approaches to mid-term load forecasting: a state-of-the-art- survey for the researcher. Int. Journal of Engineering and Technology 2(1), 94–100 (2010)
Ramanathan, R., Engle, R., Granger, C.W., Vahid-Araghi, F., Brace, C.: Short-run forecasts of electricity loads and peaks. International Journal of Forecasting 13(2), 161–174 (1997)
Cottet, R., Smith, M.: Bayesian modeling and forecasting of intraday electricity load. Journal of the American Statistical Association 98, 839–849 (2003)
Taylor, J.W., de Menezes, L.M., McSharry, P.E.: A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 22, 1–16 (2006)
Soares, L.J., Medeiros, M.C.: Modeling and forecasting short-term electricity load: A comparison of methods with an application to brazilian data. International Journal of Forecasting 24(4), 630–644 (2008)
Wulf, W.A., McKee, S.A.: Hitting the memory wall: Implications of the obvious. Computer Architecture News 23(1), 20–24 (1995)
Borkar, S.Y., Mulder, H., Dubey, P., Pawlowski, S.S., Kahn, K.C., Rattner, J.R., Kuck, D.J.: Platform 2015: Intel processor and platform evolution for the next decade. Technical report, Intel Corporation (2005)
Kim, C., Chhugani, J., Satish, N., Sedlar, E., Nguyen, A.D., Kaldewey, T., Lee, V.W., Brandt, S.A., Dubey, P.: Fast: Fast architecture sensitive tree search on modern cpus and gpus. In: Proceeding of the SIGMOD 2010 (2010)
Taylor, J.W.: Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research 204, 139–152 (2009)
Nelder, J., Mead, R.: A simplex method for function minimization. The Computer Journal 7(4), 308–313 (1965)
Nationalgrid UK: Metered half-hourly electricity demands (2010), http://www.nationalgrid.com/uk/Electricity/Data/Demand+Data/
Ge, T., Zdonik, S.: A skip-list approach for efficiently processing forecasting queries. In: Proceeding of the VLDB 2008 (2008)
Agrawal, D., Chen, D., Ji Lin, L., Shanmugasundaram, J., Vee, E.: Forecasting high-dimensional data. In: Proceeding of the SIGMOD 2010 (2010)
Cannas, B., Fanni, A., See, L., Sias, G.: Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Physics and Chemistry of the Earth 31(18), 1164–1171 (2006)
Kalaitzakis, K., Stavrakakis, G., Anagnostakis, E.: Short-term load forecasting based on artificial neural networks parallel implementation. Electric Power Systems Research 63, 185–196 (2002)
Shimokawabe, T., Aoki, T., Muroi, C., Ishida, J., Kawano, K., Endo, T., Nukada, A., Maruyama, N., Matsuoka, S.: An 80-fold speedup, 15.0 tflops full gpu acceleration of non-hydrostatic weather model asuca production code. In: Proceedings of Super Computing 2010 (2010)
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Dannecker, L., Böehm, M., Lehner, W., Hackenbroich, G. (2012). Partitioning and Multi-core Parallelization of Multi-equation Forecast Models. In: Ailamaki, A., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2012. Lecture Notes in Computer Science, vol 7338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31235-9_7
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DOI: https://doi.org/10.1007/978-3-642-31235-9_7
Publisher Name: Springer, Berlin, Heidelberg
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