7 days ago · However, a comprehensive literature review analyzing the evolution of water forecasting using machine learning and deep learning is lacking. This review should ...

7 days ago · Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made ...

7 days ago · Aiming to improve both the forecasting accuracy and interpretability of the model, a novel urban water demand forecasting neural network (UWDFNet) was presented ...

7 days ago · We investigate the preferences for the input sequences in LLMs in time series forecasting tasks. Our analysis has revealed that LLMs significantly outperform ...

6 days ago · In this study, a new forecasting model/tool is proposed for short-term load forecasting. The introduced model uses the basic advantages of neural networks and ...

7 days ago · In this paper, we use the methodology of Graph Neural Networks (GNN) to analyze the relationships between different products. GNNs were first introduced by ...

4 days ago · The main aim of this book is to describe a comprehensive set of algorithms for the identification, forecasting and analysis of nonlinear systems.

5 days ago · This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures such as Long Short-Term Memory (LSTM).

5 days ago · Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by neural circuitry.

2 days ago · In this sense, we provide a proof-of-concept study that covers a few of the main points required to construct a time series forecasting paradigm with SNNs, ...