Neural Networks For Time Series Prediction Research Articles

Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

Neural network quantization in federated learning at the edge

The massive amount of data collected in the Internet of Things (IoT) asks for effective, intelligent analytics. A recent trend supporting the use of Artificial Intelligence (AI) solutions in IoT domains is to move the computation closer to the data, i.e., from cloud-based services to edge devices. Federated learning (FL) is the primary approach adopted in this scenario to train AI-based solutions. In this work, we investigate the introduction of quantization techniques in FL to improve the efficiency of data exchange between edge servers and a cloud node. We focus on learning recurrent neural network models fed by edge data producers using the most widely adopted neural networks for time-series prediction. Experiments on public datasets show that the proposed quantization techniques in FL reduces up to 19× the volume of data exchanged between each edge server and a cloud node, with a minimal impact of around 5% on the test loss of the final model.

Real-time forecasting of petrol retail using dilated causal CNNs

The recent popularity of smart cities and smart homes has made the adoption of Internet of Things (IoT) devices ubiquitous. Most of these IoT devices are low-end devices with limited capabilities. For neural network based predictive models, the low processing power of connected things is a limitation when training them. In addition, it is still a common practice to deploy these models on cloud servers that possess dedicated high performance computing hardware. However, for IoT applications, it is not feasible to send voluminous raw data to the cloud or a remote backend server on account of high latency, information security concerns or lack of network coverage. In this work, we develop an integrated prediction system for a retail petrol station within the operational constraints of the IoT ecosystem. Our main contribution is the combination of the recent concepts of dilated convolution and the so-called causal convolution into the 1D dilated causal convolutional neural network for time-series prediction. This results in a significantly lightweight model with sound mathematical grounding. Empirical evaluation with a highly-relaxed grid search in the hyperparameter space shows an order-of-magnitude improvement over competing models, both in terms of predictive performance as well as computational cost while training. Compared to state-of-the-art models, our proposed model is able reduce the root mean squared error from 49.38 to 18.37, and training time from 93.07 to 10.21 s on the petrol sales prediction problem.

Prediction of bearing failures by the analysis of the time series

As they are a part of the energy transmission chain, bearings are considered as important mechanical components in rotating machines. This importance requires a special attention in order to avoid expensive production shutdown due to the appearance of failures. It is therefore necessary to anticipate the appearance of faults by implementing an appropriate prediction model. There exist in the literature several examples of prediction models able to estimate the remaining useful life (RUL) of bearings. These models are based on the principle of the long-term prediction without considering the degradation state of bearings or by defining a degradation threshold arbitrarily. It should be noted that, in addition to a reliable prediction model, identifying the degradation states is an important parameter in the estimation of the RUL. The presented paper proposes a particular approach based on the artificial intelligence (AI) principle. The proposed approach is composed of two model-based AI inspired by the reasoning of the human for the RUL estimation. This reasoning is modeled via the neural networks for time-series prediction. These models are called the adaptive-neuro fuzzy inference system and the neo-fuzzy neuron. The proposed approach is also composed by a third model-based AI. This model is inspired by the behavior of ants to identify the different degradation states of bearings. The combination of these two types of AI provides reliable and robust prediction results.

Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Thermal vision based intelligent system for human detection and tracking in mobile robot control system

This paper presents the results of the authors in thermal vision based mobile robot control. The most important segment of the high level control loop of mobile robot platform is an intelligent real-time algorithm for human detection and tracking. Temperature variations across same objects, air flow with different temperature gradients, reflections, person overlap while crossing each other, and many other non-linearities, uncertainty and noise, put challenges in thermal image processing and therefore the need of computationally intelligent algorithms for obtaining the efficient performance from human motion tracking system. The main goal was to enable mobile robot platform or any technical system to recognize the person in indoor environment, localize it and track it with accuracy high enough to allow adequate human-machine interaction. The developed computationally intelligent algorithms enables robust and reliable human detection and tracking based on neural network classifier and autoregressive neural network for time series prediction. Intelligent algorithm used for thermal image segmentation gives accurate inputs for classification.

Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction.

Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration, and competition are needed for CC, as multiple subpopulations are used to represent the problem. It is important to add collaboration and competition in CC. This paper presents a competitive CC method for training recurrent neural networks for chaotic time-series prediction. Two different instances of the competitive method are proposed that employs different problem decomposition methods to enforce island-based competition. The results show improvement in the performance of the proposed methods in most cases when compared with standalone CC and other methods from the literature.

Multiobjective Evolutionary Optimization of Training and Topology of Recurrent Neural Networks for Time-Series Prediction

This paper provides a new evolutionary multiobjective optimization method for automatically optimizing the network topology of recurrent neural networks (NNs). To obtain NNs with higher prediction capability for time-series data, the proposed method is constructed by focusing on the intensively exploration of a feasible region including solutions with small training errors on the Pareto frontier, unlike existing evolutionary multiobjective optimization methods, which aim to find a whole set of the Pareto optimal solutions. Our method is characterized by the ideas of self-adaptive mutation probability setting, elite preservation strategies and archive for the preservation of local optimal solutions. Through the comparison with the performances of the most promising existing method by Delgado et al. using benchmark time-series data instances, it is shown that the proposed method is superior to the existing effective algorithm with respect to the capability of time-series prediction.

A novel method for training neural networks for time-series prediction in environmental systems

Soil, streamwater and climatic variables were measured hourly over several month periods in two situations in North-East (NE) Scotland, using data loggers and other measuring instruments. One of the locations was on agricultural land near Inverness while the other was at an acidic peat moorland site in the River Dee catchment. The data sets were used to train neural networks using three different methods, including a novel, biologically plausible system. Temporal pattern recognition capabilities using each method were investigated. The novel method proved equally capable in predicting future variable values using large data sets as the other two methods. An argument is made for this method, termed the ‘Local Interaction’ method, providing valid competition to other neural network and statistical methods in the detection of patterns and prediction of events in complex biological systems.