Adoption of Optimal Time Series Models for Forecasting on New and Relapse Tuberculosis Cases in Tanzania

<i>Background:</i> Time-series models forecasting plays key role in predicting TB cases. Despite of its importance some models consist of limitation that decrease its efficiency. To overcome this, adoption of optimal model with highly proficient forecasting is encouraged. <i>Objective</i> This study was aimed to adopt an optimal Time Series model for forecasting new and relapse Tuberculosis cases in Tanzania. <i>Setting:</i> The study use ARIMA, HWES and LSTM Time series models to find optimal modal that works efficiently on forecasting of TB cases. <i>Method</i>s: A cross-sectional study was conducted at Kibong’oto National Infectious Diseases Hospital, Moshi Tanzania from January 2021 to December 2024. Muilt- stage sampling was used to recruit 3911 TB cases registered from January 2015 to December 2020. A Microsoft Excel 2019 was used to create database with total of 2 columns and 72 row. Dataset was divided into training and testing cutoff points of 69% and 31% respectively to obtain optimal time series models as per Xu & Goodacre (2018). Tables and figures were used for interpretation of results. <i>Results</i>: A total of 3911 TB cases with annual average of 651.83. The periodic variations and declines were observed. The error metric values MAE, MAPE, and RMSE show ARIMA modal better performance on forecasting the TB cases due highest scores than others modals. <i>Conclusion:</i> The ARIMA model offers advanced predictions of TB cases, that help timely planning of prevention and control measures.