Ciencias Exactas y Ciencias de la Salud

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An ensemble forecasting framework for time series
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Saldaña Rodríguez, Alejandro; REGIS HERNANDEZ, FABIOLA; 331834; Espinoza García, Juan Carlos; emipsanchez; Regis Hernández, Fabiola; Murrieta Cortés, Beatriz; Escuela de Ingenieria y Ciencias; Campus Querétaro
Forecasting for businesses is essential and, because small to medium sized enterprises cannot afford to spend the resources on accurate forecasting, the necessity to build step-by-step procedures that aid in this process is vital. Forecasting using machine learning or more complicated models comes with its own sets of challenges as many of them have parameters that are not directly interpreted to the variables. Ensemble Forecasting is a mixture between machine learning and forecasting and it uses many proven mathematical concepts such as the law of large numbers, the Jury theorem, and proven empirical evidence of these models outperforming the single models counterparts. This thesis proposes a new methodology to modernize and include the data analytics part of the cross industry standard process for data mining described in (CRISP-DM) to the time series analysis methodology proposed by George E. Box. The ensemble methods composed of linear combinations and majority-rule voting made better predictions and the new Ensemble Forecast model proposed in this thesis proved to be more accurate and precise than any other model including the other ensembling methods.

Ciencias Exactas y Ciencias de la Salud

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