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ARIMA and SVM forecasting in the US paper waste

Technology makes the lifestyle more convenient, whereas convenience could contribute to more severe waste problems. Accordingly, waste organisations can rely on adequate and accurate waste generation forecasting models to effectively solve ecological issues. This study aims to compare the forecasting methods between ARIMA and SVM models in the annual US paper/paper board waste from 1960 to 2018 based upon the time series forecasting in household paper/paper board waste generation in the United States. This research introduces the utilisation of paper and paper board waste forecasting in disposal management and general statistical methods application, and analyses the time series prediction models and detailed processes between ARIMA and SVM. The analysis in ARIMA notices the data transformation and in SVM mentions the multicollinearity issues. Besides, this study discusses and compares the performance of two approaches by measuring the accuracy with MAPE, MAE, and RMSE; the conventional statistical method, ARIMA, has lower errors than SVM. The analysis, along with the conventional statistical and machine learning approaches, shows that ARIMA is highly suggested. Finally, the suggestions in related US household paper/cardboard waste issues are put forward.