Temporal Fusion Transformer for time series forecasting

The ability to accurately forecast sales volumes holds substantial significance for businesses. Current classical models struggle in capturing the impact of different variables upon the sales volume. These machine learning models are also not applicable to more than one specific product. The Temporal Fusion Transformer (TFT) is implemented to...

Deep learning approaches to short term traffic forecasting: Capturing the spatial temporal relation in historic traffic data

The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable...

Spatio-temporal embedding in deep learning for algal bloom forecasting

The term ”Algal Bloom” refers to the accumulation of algae in a confined geological space. They may harm human health and negatively affect ecological systems around the area. Thus, forecasting algal blooms could mitigate the environmental and socio-economical damages. Particularly, the use of deep learning methods could distinguish underlying...