My project created 24 time series forecast models to predict a consumer’s next month’s electric utility bill. The focus of the project was to develop a model(s) which consistently achieved a MAPE value of <= 15% and do so while leveraging data which could be considered minimally viable. Practically speaking this meant constructing univariate models relying solely on autoregression and multivariate models with features derived from the time component of the time series itself. This path was pursued due to all available research on the use case focusing solely on producing more and more accurate models, with no attention or analysis being paid to whether the model developed would be deployable and maintainable in a production environment. The best performing model was then analyzed for the length of training history required to achieve its peak performance and thus be operationalized.