This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous
surge in research activities in the past decade. While ANNs provide a great deal of promise …

Taibai County is a mountainous area in China, where rainfall-induced landslides occur
frequently. The purpose of this study is to assess landslide susceptibility using the integrated …

Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Both theoretical and empirical findings …

Autoregressive integrated moving average (ARIMA) is one of the popular linear models in
time series forecasting during the past three decades. Recent research activities in …

Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximators that can be applied to a wide range of time series forecasting problems with a …

Time series forecasting is an important and active research area due to the significance of
prediction and decision-making in several applications. Most commonly used models for …

Forecasting the behavior of the financial market is a nontrivial task that relies on the
discovery of strong empirical regularities in observations of the system. These regularities …

This paper proposes using the support vector machines (SVMs) experts for time series
forecasting. The generalized SVMs experts have a two-stage neural network architecture. In …

Time series forecasting is an active research area that has drawn considerable attention for
applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) …