Efficiently predicting suicide rates aids resource allocation and response preparedness. This study investigates time-series data with multiple variables to model and forecast suicide events in India. Utilizing official suicide statistics (2001-2021), results highlight the superiority of the multivariate VARMA model over VAR and univariate ARIMA models. This approach uncovers overlooked patterns and a concerning upward trend in future Indian suicide incidents. The research provides insights that aid public health professionals in targeting high-need areas and enhancing readiness and suggests cause-specific preventive strategies to counter this trend.
Keywords: Forecasting model; Multivariate analysis; Preventive measures; Public health; Suicide rates; Time-series analysis.
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