Recurrent neural networks have been used for time-series prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and linear regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segment...

This research project investigates the ability of artiicial neural networks (ANNs) to predict time series. Speciically, this project examined two applications: predicting electrical power demand and earthquake tremors in Los Angeles. The ANNs modeling power demand not only predicted the next day's peak power demand, but also generated a 24-hour proole of the demand for the next day. These ANNs ...

Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series experimented with come from financial data – usually hard to forecast. One approach to prediction is to spot patterns in the past, when we already kn...

In this paper two popular time series prediction methods – the Auto Regression Moving Average (ARMA) and the multilayer perceptron (MLP) – are compared while forecasting seven real world economical time series. It is shown that the prediction accuracy of both methods is poor in ill-structured problems. In the well-structured cases, when prediction accuracy is high, the MLP predicts better provi...

Abstract: According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD) is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and ...

We introduce and discuss a local method to learn one-step-ahead predictors for iterated time series forecasting. For each single one-step-ahead prediction, our method selects among diierent alternatives a local model representation on the basis of a local cross-validation procedure. In the literature , local learning is generally used for function estimation tasks which do not take temporal beh...

Given live streaming Bitcoin activity, we aim to forecast future Bitcoin prices so as to execute profitable trades. We show that Bitcoin price data exhibit desirable properties such as stationarity and mixing. Even so, some classical time series prediction methods that exploit this behavior, such as ARIMA models, produce poor predictions and also lack a probabilistic interpretation. In light of...

An MCMC(Markov Chain Monte Carlo) algorithm is proposed for nonlinear time series prediction with Hierarchical Bayesian framework. The algorithm computes predictive mean and error bar by drawing samples from predictive distributions. The algorithm is tested against time series generated by (chaotic) Rössler system and it outperforms quadratic approximations previously proposed by the authors.

One of the important problems in many process industries is how to predict the occurrence of abnormal situations ahead of time in a multivariate time series environment. For example, in an oil refinery, hundreds of sensors (process variables) are installed at different sections of a process unit. These sensors constantly monitor the development of every stage of the process. Typically, each pro...