Enhancing sales forecasting : leveraging retail sales data for advanced ai predictive models
Ενίσχυση της πρόβλεψης πωλήσεων : αξιοποίηση δεδομένων λιανικών πωλήσεων για προηγμένα προγνωστικά μοντέλα τεχνητής νοημοσύνης
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Keywords
ΑΙ ; Artificial Intelligence ; Machine learning ; Deep learning ; Time series ; Forecasting ; Retail data ; FBProphet ; NeuralProphet ; XGBoost ; LSTM ; TFT ; TimeGPT ; ARIMA ; Predictive models ; Data preprocessing ; Sales ; Sales forecastingAbstract
This thesis aims to improve sales forecasting in the retail sector through the application
of advanced Artificial Intelligence techniques. It addresses the issue of fluctuations in
retail sales, which are influenced by a variety of external factors, including economic
changes and shifts in consumer behavior. The study develops and evaluates multiple AI
forecasting models, such as FBProphet, NeuralProphet, XGBoost, LSTM, TFT and
TimeGPT, to enhance the accuracy and flexibility of predictions. Moreover, the thesis
provides an in-depth comparison between conventional time series forecasting methods
such as ARIMA and the aforementioned machine and deep learning approaches. The
findings underscore the superior performance of state-of the-art AI-based models in
handling complex patterns and adapting to new data, thereby providing more accurate
and adaptable sales forecasts. Additionally, the thesis emphasizes the importance and
impact of thorough data preprocessing.