Autoregressive Recurrent Neural Network Research Articles

Nonlinear Autoregressive Recurrent Neural Network Model for Quality of Service Prediction

Due to the advances in computer networks, Internet and multimedia communications, Quality of Service (QoS) monitoring and assessment become an increasingly important. The importance of assessing QoS stems from the fact it may reflect the resources availability of a network that may provide solutions for QoS provision, routing, monitoring, management … etc. In the literature, several monitoring and measurement approached and methods have been developed to quantify and predict the QoS of multimedia applications transmitted over such networks. In this research, a new QoS prediction system will be designed. The proposed system is based on using the Nonlinear Autoregressive with eXogenous input model (NARX) using recurrent neural network. This prediction system uses in addition to the QoS parameters; previous measured QoS values will used as inputs to this model. The expected output of this new model is the forecasted QoS. The proposed model will be trained, tested, validated and then optimized to provide a good estimate of the QoS provided by the given network. Simulation results are expected to show that the proposed model will have high accurate QoS prediction capabilities compared to other QoS assessment systems adopted in the literature.

Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link.

The computational complexity and system bit-error-rate (BER) performance of four types of neural-network-based nonlinear equalizers are analyzed for a 50-Gb/s pulse amplitude modulation (PAM)-4 direct-detection (DD) optical link. The four types are feedforward neural networks (F-NN), radial basis function neural networks (RBF-NN), auto-regressive recurrent neural networks (AR-RNN) and layer-recurrent neural networks (L-RNN). Numerical results show that, for a fixed BER threshold, the AR-RNN-based equalizers have the lowest computational complexity. Amongst all the nonlinear NN-based equalizers with the same number of inputs and hidden neurons, F-NN-based equalizers have the lowest computational complexity while the AR-RNN-based equalizers exhibit the best BER performance. Compared with F-NN or RNN, RBF-NN tends to require more hidden neurons with the increase of the number of inputs, making it not suitable for long fiber transmission distance. We also demonstrate that only a few tens of multiplications per symbol are needed for NN-based equalizers to guarantee a good BER performance. This relatively low computational complexity signifies that various NN-based equalizers can be potentially implemented in real time. More broadly, this paper provides guidelines for selecting a suitable NN-based equalizer based on BER and computational complexity requirements.

DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

Hourly global solar irradiation forecasting for New Zealand

With the growing use of solar thermal energy systems and small scale photovoltaic power generation by domestic users, there is increasing need to develop intelligent controllers that allow these users to efficiently manage the energy generated by these systems. Ideally these intelligent controllers will be able to forecast the availability and magnitude of the solar resource to plan in advance for periods when the solar irradiance magnitude is small or unavailable. In addition, the method used to provide this forecast needs to be adaptable to a range of timescales and locations. With this in mind, this study examined the possibility of providing a 24-h ahead forecast of hourly global solar irradiation in New Zealand using several approaches but with particular reference to nonlinear autoregressive recurrent neural networks with exogenous inputs (NARX).Hourly time series data for nine historic weather variables recorded over a three year period was used to train and test the forecasting methods for New Zealand’s largest city, Auckland. Results from forecasts based on the NARX were compared with an artificial neural network (ANN) based Multilayer Perceptron (MLP) method, a statistical approach using auto regressive moving average (ARMA) and a reference persistence approach. Predicted values of hourly global solar irradiation were compared with the measured values, and it was found that the root mean squared error (RMSE) was 0.243MJ/m2 for the NARX method as compared to 0.484MJ/m2, 0.315MJ/m2 and 0.514MJ/m2 for the MLP, ARMA and persistence approaches respectively. Subsequently the NARX approach was used to forecast global solar irradiation for other major cities across New Zealand. The results demonstrate the ability of the NARX approach to forecast irradiation values at a later time and across a number of different locations. As such it is foreseeable that such an approach could serve as the basis of a forecasting system in future intelligent controllers.

Forecasting the monthly abundance of anchovies in the South Sea of Korea using a univariate approach

Japanese anchovies, Engraulis japonicus, have exhibited substantial fluctuations in production in Korean waters. Anchovy drag net and drift gillnet fisheries are the major types of fishery that target different sizes of anchovies during different fishing seasons. We analyzed the monthly catch per unit effort (CPUE) for 1987–2012 using exponential smoothing methods, seasonal autoregressive integrated moving average (SARIMA), autoregressive neural network (ANN), and autoregressive recurrent neural network (ARNN) models to forecast the abundance of anchovies based on the fishing conditions. For the drag net fisheries, SARIMA provided better statistical insight than the other models, but the ARNN model was best for future forecasting (r2=0.819, PI=0.733). Additive Winters’ exponential smoothing (AWES) was the most effective of the three smoothing methods, but its validation was poor compared with SARIMA and the other neural network models. AWES, SARIMA, ANN and ARNN were less suitable for the drift gillnet fisheries. A comparison of the CPUE of drift gillnet fisheries to that of drag net fisheries showed that the data had been contaminated by factors such as periods when anchovy fishing was prohibited or a fishery had been abandoned due to economic reasons, rather than biological factors. Nevertheless, ARNN proved to be an effective and accurate model in the training phase, and its forecasts showed a comparatively strong statistical performance (r2=0.797, PI=0.662) in the context of short-to-medium-length time periods. Additionally, whereas SARIMA performed worse than ANN or ARNN, its forecasting capability was comparatively satisfactory (r2=0.713, PI=0.584). Finally, SARIMA has the advantage of providing statistical descriptions of the catches.

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neural-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network auto-regressive models of the PAM manipulator with different number of neurons in hidden layer is completely discussed. On this basis, the merits of each identified model of the highly nonlinear PAM manipulator have been analyzed and compared. The results show that the nonlinear NNARX model yields better performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. These results can be applied to model and identify not only the PAM manipulator but also to control other nonlinear and time-varying industrial systems.

Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics

This study was aimed at developing a Temporal Autoregressive Recurrent Neural Network (TARNN) model that could predict time-series changes of phytoplankton dynamics in a regulated river ecosystem in South Korea. In recent years, a large quantity of ecological data has been globally accumulated in habitats monitored by Long-Term Ecological Research (LTER), and that data enabled ecologists to apply non-linear data-driven ecological modelling algorithms to their systems. Numerous studies have reported that empirical modelling algorithms such as Artificial Neural Networks (ANNs) were superior to conventional models in applicability, especially for systems where underlying ecological relationship was not fully understood. However, in order to process sufficient information from the target ecosystem or entity, the size of empirical models tended to become larger by applying diverse state variables or forcing functions to the models. Despite the fact that the developed models empirically had accurate performance in prediction or classification of target data, they are occasionally not free from complex structures (e.g., large number of input variables). The TARNN algorithm used in this study can be an alternative solution to overcome the increasing size and structural complexity of the models, based on model performance, which is hardly found in freshwater ecology. In this study, the performance of TARNN for freshwater ecological data was evaluated by being applied to two types of data sets of phytoplankton dynamics. The first data encompassed intensive seasonality (monthly averaged biovolume of Stephanodiscus hantzschii) which proliferated and dominated the algal assemblage (ca. 90%) annually in the lower Nakdong River during winters. The other data was daily sampled phytoplankton biomass (chlorophyll a) in which significant seasonality did not reside. The predictability of two TARNN models was compared with appropriate statistical models, i.e., data with seasonality was modelled by Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and the other data with Simple Exponential Smoothing. In both cases of data sets, the TARNN models outperformed the statistical methods in accuracy of prediction (TARNN, r 2 > 0.9 for both seasonal and non-seasonal testing data sets; both statistical models, r 2 < 0.3 for the testing data sets). Especially, the capacity of prediction using the TARNN algorithm was not decreased by the absence of seasonality. Through the presented results, it can be assumed that TARNN can process non-linear autoregressive analysis successfully for ecological data. The development of non-linear autoregressive models for phytoplankton prediction can suggest more efficient direction for water quality management approaches of freshwater systems with long-term database. A real-time forecasting system for algal proliferation, one of the state-of-the-art predicting systems, can be identified using the linkage between the modelling technique and telemetry.