Data preprocessing is a crucial step for mining and learning from data, and one of its primary
activities is the transformation of data. This activity is very important in the context of time …

The ability to predict short-term electric energy demand would provide several benefits, both
at the economic and environmental level. For example, it would allow for an efficient use of …

Time series forecasting using machine learning algorithms has gained popularity recently.
Random forest is a machine learning algorithm implemented in time series forecasting; …

Smart buildings are equipped with sensors that allow monitoring a range of building systems
including heating and air conditioning, lighting and the general electric energy consumption …

It is widely known from energy balances that global oceans play a fundamental role in
atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather …

The electric energy production would be much more efficient if accurate estimations of the
future demand were available, since these would allow allocating only the resources …

Ethanol serves as one of Brazils primary biofuels. The country produces two main types of
ethanol: i) hydrous ethanol, directly utilized as vehicle fuel, and ii) anhydrous ethanol …

Prediction is one of the most important activities while working with time series. There are
many alternative ways to model the time series. Finding the right one is challenging to model …

The nonstationary time series prediction is challenging since it demands knowledge of both
data transformation and prediction methods. This paper presents TSPred, a framework for …

Demand forecasting for sales is a widely researched topic that is essential for a business to
prepare for market changes and increase profits. Existing research primarily focus on data …