Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

Optimising HVAC operations towards human wellness and energy efficiency is a major
challenge for smart facilities management, especially amid COVID situations. Although IoT …

Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …

In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …

Time series forecasting (TSF) is the task of predicting future values of a given sequence
using historical data. Recently, this task has attracted the attention of researchers in the area …

Time series forecasting is an important technique to study the behavior of temporal data and
forecast future values, which is widely applied in many fields, eg air quality forecasting …

Abstract Machine learning has gained increasing popularity in building energy management
due to its powerful capability and flexibility in model development as well as the rich data …

Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …