In this paper, we propose a new short-term load forecasting (STLF) model based on
contextually enhanced hybrid and hierarchical architecture combining exponential …

The secure operation of the transmission grid is of primary importance for any power system
operator. However, the introduction of new technologies, market deregulation, and …

Background Contemporary data sets are frequently relational in nature. In retail, for
example, data sets are more granular than traditional data, often indexing individual …

Financial time series data forecasting is difficult since the data typically exhibit complicated
characteristics such high non-linearity and non-smoothness and a lot of noise. In order to do …

Several interrelated variables typically characterize real-world processes, and a time series
cannot be predicted without considering the influence that other time series might have on …

Contemporary data sets are frequently relational in nature. In retail, for example, data sets
are more granular than traditional data, often indexing individual products, outlets, or even …

Conventional approaches in times series literature are restricted to low-dimension series,
linear methods and short horizons. Big data revolution is instead shifting the focus to …

Time series forecasting deals with the prediction of future values of time-dependent
quantities (eg stock price, energy load, city traffic) on the basis of their historical …

Conventional approaches in times series literature are restricted to lowdimension series,
linear methods and short horizons. Big data revolution is instead shifting the focus to …