Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …

Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

Real-time crash risk prediction is expected to play a crucial role in preventing traffic
accidents. However, most existing studies only focus on freeways rather than urban arterials …

Developing accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …

We explore the architecture of recurrent neural networks (RNNs) by studying the complexity
of string sequences that it is able to memorize. Symbolic sequences of different complexity …

This paper contributes a new machine learning solution for stock movement prediction,
which aims to predict whether the price of a stock will be up or down in the near future. The …

For recurrent neural networks trained on time series with target and exogenous variables, in
addition to accurate prediction, it is also desired to provide interpretable insights into the …

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …

Deep neural networks have achieved promising results in stock trend prediction. However,
most of these models have two common drawbacks, including (i) current methods are not …

Time series prediction is an intensively studied topic in data mining. In spite of the
considerable improvements, recent deep learning-based methods overlook the existence of …