The memory concept behind deep neural network models: an application in time series forecasting in the e-Commerce sector
FR Ramos, MT Pereira, M Oliveira… - … Making: Applications in …, 2023 - dmame-journal.org
A good command of computational and statistical tools has proven advantageous when
modelling and forecasting time series. According to recent literature, neural networks with …
modelling and forecasting time series. According to recent literature, neural networks with …
Forecasting bitcoin volatility: exploring the potential of deep learning
This study aims to evaluate forecasting properties of classic methodologies (ARCH and
GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM …
GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM …
Deep neural networks: A hybrid approach using box&jenkins methodology
FR Ramos, DR Lopes, TE Pratas - International Conference Innovation in …, 2022 - Springer
The articulation of statistics, mathematical and computational techniques for modelling and
forecasting of time series can help in the decision-making process. When dealing with the …
forecasting of time series can help in the decision-making process. When dealing with the …
Applying Deep Learning Techniques to Forecast Purchases in the Portuguese National Health Service
J Sequeiros, FR Ramos, MT Pereira, M Oliveira… - Congress of the …, 2022 - Springer
Forecasting plays a crucial role in enhancing the efficiency and effectiveness of logistics and
supply chain management in the healthcare sector, particularly in financial management …
supply chain management in the healthcare sector, particularly in financial management …