Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data
Spatio-temporal model forecasting method is a forecasting model that combines forecasting with a function of time and space. This method is expected to be able to answer the challenge to produce more accurate and representative forecasting. Using the ability of method Support Vector Regression in dealing with data that is mostly patterned non-linear premises n adding a spatial element in the model of forecasting in the form of a model forecasting Spatio- Temporal. Some simulations have done with generating data that follows the Threshold Autoregressive model. The models are correlated into spatial points generated by several sampling methods. Simulation models are generated to comparing the accuracy between model Spatio-Temporal Support Vector Regression and model ARIMA based on Mean Error, Mean Average Error, Root Mean Square Error, and Mean Average Percentage Error. Based on the evaluation results, it is shown that forecasting with the Spatio-Temporal Support Vector Regression model has better accuracy than forecasting ARIMA.
Abu Awad, Y., Koutrakis, P., Coull, B. A., & Schwartz, J. (2017). A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States. Environmental Research, 159 (Agustus), 427–434.
Borst, Richard. (2013). A Space-Time Model for Computer Assisted Mass Appraisal. Aestimum. 10.13128/Aestimum-13160.
Cheng, T., & Wang, J. (2006). Applications of spatio-temporal data mining and knowledge discovery (STDMKD) for forest fire prevention. Proc. of Multitemporal Data and Change Detection,
Cheng, T., Wang, J., & Li, X. (2007). The Support Vector Machine for Nonlinear Spatio-Temporal Regression. Proceedings of Geocomputation 2007, 1–6.
Kamarianakis, Y. (2003). Spatial-Time Series Modeling: a Review of the Proposed Methodologies. Phone Fax, (217217), 61801–63671.
Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). kernlab – An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1–20.
Kirono, S. (2016). Spatio-Temporal Sequential Pattern Mining Untuk Deteksi Dini Kebakaran Pada Lahan Gambut Di Provinsi Riau.
Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1997). Forecasting Methods and Applications (3rd ed.). New York: Willey.
Mohan, A. (2014). A New Spatio-Temporal Data Mining Method and its Application to Reservoir System Operation.
Nugroho, A. S., Witarto, A. B., & Handoko, D. (2003). Support vector machine: Teori dan Aplikasinya dalam Bioinformatika. IlmuKomputer.Com.
Ojemakinde, B. T. (2006). Support Vector Regression for Non-Stationary Time Series.
Retnaningrum. (2015). Penerapan Model Star (Space Time Autoregressive) dan Arima (Autoregressive Integrated Moving Average) untuk Peramalan Data Curah Hujan Di Kabupaten Jember [Skripsi]. Jember: Universitas Jember.
Smith, T. E. (2016). Notebook on Spatial Data Analysis [online].
Supranto, J. (2008). Statistik: Teori dan aplikasi (7th ed., Vol. 1). Jakarta: Erlangga.
Tong, H. (1983). Threshold models in non-linear time series analysis. New York: Springer
Vapnik, V. N. (2000). The nature of statistical learning theory (2nd ed.). New York, USA: Springer-verlag.
Wang, J., Cheng, T., & Li, X. (2007). Nonlinear Integration of Spatial and Temporal Forecasting by Support Vector Machines. Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), (Fskd), 61–66.
Wang, J. Q., Cheng, T., & Haworth, J. (2012). Space-Time Kernels. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(2), 57–62.
Wikle, C. K. (2015). Modern perspectives on statistics for spatio-temporal data. Wiley Interdisciplinary Reviews: Computational Statistics, 7(1), 86–98.
Xu, Y., Wang, B., & Wang, F. (2014). Spatio-temporal Variable Selection Based Support Vector Regression for Urban Traffic Flow Prediction.
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