Abstract:PURPOSE The purpose of this study was to apply different time series analytical techniques to SEER US lung cancer death rate data to develop a best fit model. METHODS Three models for yearly time series predictions were built: autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt's double expansional smoothing (HDES) models. The three models were built using Python 3.9, on the basis of Anaconda 2022.10. RESULTS This study used SEER data from 1975 to 2018 and included 54… Show more
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