Weather forecasting is a challenging time series forecasting problem because of its dynamic, continuous, data-intensive, chaotic and irregular behavior. At present, enormous time series forecasting techniques exist and are widely adapted. However, competitive research is still going on to improve the methods and techniques for accurate forecasting. This research article presents the time series...

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...

Abstract Mill chatter seriously restricts the improvement of production efficiency and development new products. Thus, a method cold rolling monitoring early warning is proposed based on combination Functional Data Analysis (FDA) General Autoregression Model (GAM). Firstly, multi-source data are preprocessed by FDA to realize smooth fitting unequally sampled sample space constructed mechanism. ...

To improve the prediction accuracy of short-term load series, this paper proposes a hybrid model based on multi-trait-driven methodology and secondary decomposition. In detail, four steps were performed sequentially, i.e., data decomposition, individual prediction, ensemble output, all which designed methodology. particular, multi-period identification method judgment basis decomposition to ass...

In this paper a Polychronous Spiking Network was applied to financial time series prediction with the aim of exploiting the inherent temporal capabilities of the spiking neural model. The performance of this network was benchmarked against two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron network and a Functional Link Neural Network. Three non-stationary datasets were u...

Predicting traffic generated by multimedia sources is needed for effective dynamic bandwidth allocation and for multimedia quality-of-service (QoS) control strategies implemented at the network edges. The time-series representing frame or visual object plane (VOP) sizes of an MPEG-coded stream is extremely noisy, and it has very long-range time dependencies. This paper provides an approach for ...

Multi –Step ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multi-step chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performa...

Multi step prediction is a complex task that has attracted increasing interest in recent years. The contribution in this work is the development of nonlinear neural network models for the purpose of building multi step Prediction of Internet Bandwidth i.e. bits per second transmission record of server. It is observed that such problems exhibit a rich chaotic behavior and also leads to strange a...

Precise forecasting of reference evapotranspiration (ET0) is one the critical initial steps in determining crop water requirements, which contributes to reliable management and long-term planning world’s scarce sources. This study provides daily prediction multi-step forward ET0 utilizing a long short-term memory network (LSTM) bi-directional LSTM (Bi-LSTM) model. For predictions, model’s accur...