Predicting the direction of stock price changes is an important factor, as it contributes to the
development of effective strategies for stock exchange transactions and attracts much …

Forecasting of energy-related variables is crucial for accurate planning and management of
electrical power grids, aiming at improving overall efficiency and performance. In this paper …

In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is applied to the
problem of long-term time series prediction. Three known strategies for the long-term time …

Since the late nineties, the Basel Accords require financial institutions to measure their
financial risk by reporting daily predictions of Value at Risk (VaR) based on 10‐day returns …

The Weibull, log-logistic and log-normal distributions are extensively used to model time-to-
event data. The Weibull family accommodates only monotone hazard rates, whereas the log …

How much electricity is going to be consumed for the next 24 hours? What will be the
temperature for the next three days? What will be the number of sales of a certain product for …

Exponential smoothing has been one of the most popular forecasting methods used to
support various decisions in organizations, in activities such as inventory management …

Efficient management of a drinking water network reduces the economic costs related to
water production and transport (pumping). Model predictive control (MPC) is nowadays a …

We introduce StoDynProg, a small library created to solve Optimal Control problems arising
in the management of Renewable Power Sources, in particular when coupled with an …

针对传感网环境监测应用采集的时间序列数据, 提出了一种新的基于高斯过程模型的多步预测
方法, 实现了对未来时刻的环境监测数据的预测. 高斯过程模型通过核函数描述数据的特性 …