Abstract
Nowadays, time-series modelling techniques are widely used for prediction and forecasting of non-stationary data’s. The study analyses the continuous one-year ambient noise data using SVM and ARIMA modelling technique. The application of these techniques has been reported in time-series prediction and forecasting. A case study of each site of commercial is utilised to train the model. In SVM, tenfold cross-validation has been used to ascertain the optimum value of hyper-parameters (γ, ε, C). Box-Jerkin ARIMA technique has been also considered to simulate ambient day and night noise levels. Several statistical parameters such as MSE, RMSE, MAPE and R2 were used to ascertain the performance of proposed models. It was observed that SVM model outperforms ARIMA models.
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Acknowledgements
Authors are thankful to Director, CSIR-NPL, New Delhi and Head, Physico-Mechanical Metrology department, and Head Acoustics and Vibration standards for allowing to work in Acoustics and Vibration Metrology division and the Head of Mining Machinery department IIT(ISM), Dhanbad for the support throughout the study.
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Tiwari, S.K., Kumaraswamidhas, L.A., Garg, N. (2022). Comparison of SVM and ARIMA Model in Time-Series Forecasting of Ambient Noise Levels. In: Bansal, R.C., Agarwal, A., Jadoun, V.K. (eds) Advances in Energy Technology. Lecture Notes in Electrical Engineering, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-16-1476-7_69
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