Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water
resources variables. In this paper, the steps that should be followed in the development of …

Due to the inherent non-linearity and non-stationary characteristics of financial stock market
price time series, conventional modeling techniques such as the Box–Jenkins …

The need for increased accuracies in time series forecasting has motivated the researchers
to develop innovative models. In this paper, a new hybrid time series neural network model …

This paper proposes a hybrid forecasting model, which combines the seasonal time series
ARIMA (SARIMA) and the neural network back propagation (BP) models, known as …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Support vector regression has been applied to stock market forecasting problems. However,
it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple …

This paper presents a comparative study of the predictive performances of neural network
time series models for forecasting failures and reliability in engine systems. Traditionally …

A multilayer feed‐forward neural network, trained with a gradient descent, back‐propagation
algorithm, is used to estimate the favourability for gold deposits using a raster GIS database …

Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions,
are extremely complex processes that are affected by multiple factors. The relationships …

Fast fashion is a commonly adopted strategy in fashion retailing. Under fast fashion,
operational decisions have to be made with a tight schedule and the corresponding …