There is a need for more effective and efficient seasonally-adjusted predictive data analysis solutions. This need can be addressed by, for example, solutions for performing seasonally-adjusted predictive data analysis that use autoregressive integrated moving average (ARIMA) machine learning models. In one example, a method includes: identifying a seasonally-adjusted training input timeseries data object and a seasonally-adjusted testing timeseries data object; generating a trained ARIMA machine learning model using the seasonally-adjusted training input timeseries data object; determining a validation determination for the trained ARIMA machine learning model based on the seasonally-adjusted testing timeseries data object; determining whether the validation determination describes a positive validation; and in response to determining that the validation determination describes the positive validation, enabling performance of a predictive inference using the trained ARIMA machine learning model in order to generate one or more prospective time-lagged predictions and to perform prediction-based actions based on the prospective time-lagged predictions.