Abstract
Since past decades, tremendous amount of data is being generated and transmitted because of Internet of Things revolution. In agriculture as well as many industrial services, close monitoring of surrounding helps in taking effective decisions at near real time. Focus is much upon forecasting weather in such environments. Many algorithms are being utilized for performing predictions. Linear regressions give satisfactory results. Also time-series models tend to give poor results for nonlinear nature of data. To overcome this drawbacks support vector machines as a regression is being used. This paper gives a brief insight into the way support vector machines can be used for prediction which gives much better prediction results for linear as well as nonlinear nature of data. With proper tuning of model and appropriate kernel selection, around 98% of accurate prediction has been achieved.
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Chavan, G., Momin, B. (2019). A Novel Approach for Forecasting the Linear and Nonlinear Weather Data Using Support Vector Regression. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_43
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DOI: https://doi.org/10.1007/978-981-10-8639-7_43
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