Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31181
Prediction of Research Topics Using Ensemble of Best Predictors from Similar Dataset

Authors: Indra Budi, Rizal Fathoni Aji, Agus Widodo

Abstract:

Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.

Keywords: Forecasting, Machine Learning, Time series, Research Topics, prediction, Ensemble, similarity measure, combination, emerging topics

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090711

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1586

References:


[1] J. S. Armstrong, "Combining Forecasts”, Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, 2001.
[2] D. Berndt, & J. Clifford, J., "Using dynamic time warping to find patterns in time series”. AAAI-94 Workshop on Knowledge Discovery in Databases (KDD-94), Seattle, Washington, 1994.
[3] S. F. Crone, & N. Kourentzes, "Forecasting Seasonal Time Series with Multilayer Perceptrons – an Empirical Evaluation of Input Vector Specifications for Deterministic Seasonality”, Lancaster University Management School, Lancaster, UK, 2007.
[4] T. Felty, "Dynamic Time Warping”, toolbox available at Mathlab Central, 2005.
[5] S. R. Gunn, "Support Vector Machines for Classification and Regression”, Technical Report, University Of Southampton, 10 May 1998.
[6] Hassan, M. R., 2007. "Hybrid HMM and Soft Computing modeling with applications to time series analysis”, PhD thesis, The University of Melbourne, 2007.
[7] C. Huang, D. Yang, Y. Chuang, Y., "Application of wrapper approach and composite classifier to the stock trend prediction”, Elsevier, Expert Systems with Applications 34 (2008) 2870–2878.
[8] S. Makridakis, S. C. Wheelwright, V. E. McGee, "Forecasting: Methods and Applications”, 2nd Ed, John Wiley & Sons, 1983.
[9] P. Poncela, J. Rodrıgueza, R. Sanchez-Mangasa, E. Senra, "Forecast combination through dimension reduction techniques”, International Journal of Forecasting 27 (2011) 224–237.
[10] K. Siwek, S. Osowski, S., R. Szupiluk, R., "Ensemble Neural Network Approach For Accurate Load Forecasting In A Power System”, Int. J. Appl. Math. Comput. Sci., 2009, Vol. 19, No. 2, 303–315
[11] A. Timmerman, "Forecast Combinations”, UCSD, 2005.
[12] M. Vlachos, "A practical Time-Series Tutorial with MATLAB”, ECML PKDD, Porto, Portugal, 2005.
[13] A. Widodo, and I. Budi, "Clustering Patent Document in the Field of ICT”, International Conference on Semantic Technology and Information Retrieval, Malaysia, 28-29 June, 2011.
[14] A. Widodo, and I. Budi, "Combination of Time Series Forecasts using Neural Network”, Conference on Electrical Engineering and Informatics, ITB-Bandung, 18-19 July, 2011.
[15] G. Q. Zang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural network: The state of the art”, International Journal of Forecasting, vol. 14, no 1, pp 35062, March 1998.
[16] R. R. Andrawis, A. F. Atiya, H. El-Shishiny, "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition”, International Journal of Forecasting, 2011.
[17] E. Gonzalez-Romera, M. A. Jaramillo-Moran, and D. Carmona-Fernandez, "Monthly electric energy demand forecasting based on trend extraction”, IEEE Transations on Power Systems 21(4): 1946–1953, 2006.
[18] P. H. Franses, "Model selection for forecast combination”, Econometric Institute Research Papers, Erasmus University Rotterdam, June 2008.
[19] JR. Meredith, SJ. Mantel, "Technological Forecasting”, John Wiley & Sons, Inc., 1995.
[20] M. Bengisu, R. Nekhili, "Forecasting emerging technologies with the aid of science and technology databases”. Technological Forecasting & Social Change 2006; 73: 835–844.
[21] H. Small, "Tracking and predicting growth areas in science”. Scientometrics, 2006, 68(3):595–610.
[22] E.R. Rahayu, and ZA. Hasibuan, "Identification of technology trend on Indonesian patent documents and research reports on chemistry and metallurgy fields”. Proceeding Asia Pacific Conf., Singapore, 2006.
[23] TU. Daim, G Rueda, H Martin, and P Gerdsri, "Forecasting emerging technologies: Use of bibliometrics and patent analysis”. Technological Forecasting and Social Change, October 2006, 73(8):981–1012.
[24] W.L Woon, A. Hensche, and S. Madnick, "A Framework For Technology Forecasting And Visualization”. Working Paper Series, ESD-WP-2009-16, October 2009.
[25] B.E. Ziegler. "Methods for Bibliometric Analysis of Research: Renewable Energy Case Study”. Working Paper CISL#2009-10, September 2009.
[26] G. Vidican, W.L. Woon, S. Madnick, "Measuring Innovation Using Bibliometric Techniques: The Case of Solar Photovoltaic Industry”, Working Paper CISL# 2009-05, 2009.
[27] S. Jun, D. Uhm. "Technology Forecasting Using Frequency Time Series Model: Bio-Technology Patent Analysis”. Journal of Modern Mathematics & Statistics, 2010, 4(3):101-104.
[28] A. Widodo., M.I. Fanany, I. Budi, "Technology Forecasting in the Field of Apnea from Online Publications: Time Series Analysis on Latent Semantic”. International Conference on Digital Information Management, 26-28 September 2011, Melbourne, Australia.
[29] C. Christodoulos, C. Michalakelis, D. Varoutas. "Forecasting with limited data: Combining ARIMA and diffusion models”. Technological Forecasting & Social Change 2010, 77: 558–565.