Solar Irradiance Prediction using Neural Model

The accurate prediction of solar irradiation has been a leading problem for better energy scheduling approach. Hence in this paper, an Artificial neural network based solar irradiance is proposed for five days duration the data is obtained from National Renewable Energy Laboratory, USA and the simulation were performed using MATLAB 2013. It was found that the neural model was able to predict the solar irradiance with a mean square error of 0.0355

Curve fitting predication with artificial neural networks: A comparative analysis

Artificial neural networks (ANN) is considered one of the most efficient methods in processing Big Data, they have a great potential in economics and engineering applications. The aims of this paper is to discuss the best method for forecasting time series by comparing the results of ANN and Box and Jenkins methods (BJ) or ARMA models. As well as finding the best curve fitting and forecasting for linear or semi linear model. In this paper uses 3 error indicators to measure the efficiency of forecasting for the forecasting performance. The most important conclusion of this paper Proved that artificial neural networks are more effective than Box-Jenkins method or ARMA models in solving time series. The results also proved that artificial neural networks are significantly improving errors in the results and this is the ambition of all researchers

Forecasting Future Customer Call Volumes: Case study

Forecasting future volumes of customer calls in call centers has proved to be a tedious and challenging task. This study, using time series analysis proposes two adequate ARIMA (p, d, q) models that are suitable to forecast two volumes of customer calls, IVR Hits Volumes and Offered Call volumes. 1472 times series data points from date 01/01/2014 to 11/01/2018 were obtained from a call center based in Kenya on the two variables of interest (IVR Hits Volumes and Offered Call volumes). The appropriate orders of the two models are picked based on the examination of the results of the ACF and PACF plots. The AIC criterion is used to select the best model for the data. The best ARIMA model for log IVR Hits volumes is ARIMA (5, 1, 3) with and the best ARIMA model for log Offered Call Volumes is ARIMA (6, 1, 3) with . The two models are recommended to model and forecast the daily arrival volumes of customer call data. The obtained forecast will be used in providing insights for appropriate workforce management

Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction

Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function is more superior in learning the high traffic pattern with a learning rate of 0.9. In addition, Re-LU is more powerful to be used in the first order in terms of combination. Hence, combining a high learning rate and pure ReLU, ReLu-sigmoid, or ReLu-Tanh is more suitable and recommended to predict upper traffic utilizatio

Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis - A Data Driven Approach

The consequences of un-clean water are some of the direst issues faced by humanity today. These concerns can be addressed efficiently if data is pre-analyzed and water quality is predicted before its effects occur. The aim of this research is to develop a novel ensemble of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using averaging ensemble technique, producing improved prediction accuracy. Measurements of different water quality parameters have been used for predicting the overall water quality, applying ANN, ANFIS and ANN-ANFIS ensemble and their results have been compared. The data used in this study is obtained by USGS online repository for the year of 2015, with a 30-minutes time interval between measurements. Root Mean Squared Error (RMSE) has been used as the main performance measure. The results depict a significant improvement in the Ensemble ANN-ANFIS model (RMSE: 0.457) as compared to both the ANN model (RMSE: 2.709) and the ANFIS model (1.734). The study concludes that the ensemble of ANN and ANFIS model shows significant improvement in prediction performance as compared to the individual models. The research can prove to be beneficial for decision making in terms of water quality improvement

An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors

A Review of the Anthropogenic Global Warming Consensus: An Econometric Forecast Based on the ARIMA Model of Paleoclimate Series

This paper projects a climate change scenario using a stochastic paleotemperature time series model and compares it to the prevailing consensus using Autoregressive Integrated Moving Average Process Model (ARIMA). The parameter estimates of the model were below that established by the anthropogenic experts and governmental organs, such as the IPCC (UN) over a 100-year scenario. Results from the ARIMA model suggest a current period of temperature reduction and a probable cooling. The results from this study add a statistical element of paleoclimate to the debate that contradicts the current scientific consensus

Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models

Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.Comment: The paper is the accepted version of our work in the 4th IEEE International Conference on Electronics, Communication, and Aerospace Technology (ICECA'20), November 5 - 7, 2020, Coimbatore, INDIA, The paper consists of 10 pages. It contains 12 figures and 8 table

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe