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Publicação
Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.67 MB | Adobe PDF |
Orientador(es)
Resumo(s)
This internship report introduces an innovative research investigation in the field of revenue forecasting in corporate settings, utilising python, and sophisticated machine learning algorithms to outperform conventional forecasting approaches. This study employs a novel methodology by utilising different combinations of machine learning models and optimising parameters to improve the precision of predictive models. The implementations resulted in a considerable improvement in the prediction accuracy, making this application a reliable source of revenue prediction for Siemens. Despite the inherent constraints associated with the amount and variety of input data, like the limitation of historical data, this study displays a noteworthy enhancement in time series prediction, surpassing traditional human methods. This dissertation presents an original approach that offers a realistic demonstration of the application and efficacy of advanced machine learning techniques in the domain of revenue forecasting. The results provide significant insights and a solid basis for future advancements in the field of business analytics, hence facilitating the creation of more sophisticated and effective digital revenue forecasting systems.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Forecasting Machine Learning Models Python Time Series Siemens Business Analytics Revenue Forecasting