Modelo double exponential smoothing para pronosticar saldos diarios de algunos productos en BBVA

Abstract

In the financial context, accurate balance forecasting is crucial for strategic and operational decision-making. This thesis focuses on the application of the Double Exponential Smoothing (DES) model as an effective tool for predicting daily balances for selected products at BBVA. The project was developed in several stages. First, a construction and adjustment process was carried out based on historical daily balance data, identifying the structure to be used in our forecasts. Based on this construction, the double exponential smoothing model was selected due to its ability to handle data with linear trends and its computational simplicity. Finally, the model's performance is observed and confidence intervals are constructed by combining the bootstrap and standard normal distributions.