Implementing Spectral Decomposition of Time Series Data in Artificial Neural Networks to Predict Air Pollutant Concentrations

2015 ◽  
Vol 32 (5) ◽  
pp. 379-388 ◽  
Author(s):  
Nima Kamali ◽  
Maryam Zare Shahne ◽  
Mohammad Arhami
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Spectral Decomposition ◽  
Time Series Data ◽  
Air Pollutant ◽  
Series Data ◽  
Pollutant Concentrations ◽  
Artificial Neural ◽  
Air Pollutant Concentrations

scholarly journals Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Narayanan Manikandan ◽  
Srinivasan Subha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Stock Prices ◽  
Time Series Data ◽  
Financial Analysis ◽  
Parallel Implementation ◽  
Series Data ◽  
Artificial Neural

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

scholarly journals Deep Learning and Autoregressive Approach for Prediction of Time Series Data

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Statistical Models ◽  
Time Complexity ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach ◽  
Proposed Model ◽  
Artificial Neural

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.

scholarly journals Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis

2016 ◽  
Vol 75 (4) ◽  
pp. 765-774
Author(s):  
Leonardo Plazas-Nossa ◽  
Thomas Hofer ◽  
Günter Gruber ◽  
Andres Torres
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Series Data ◽  
Visible Range ◽  
Artificial Neural ◽  
Uv Range

This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

scholarly journals Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 2393
Author(s):  
Md Mijanur Rahman ◽  
Mohammad Shakeri ◽  
Sieh Kiong Tiong ◽  
Fatema Khatun ◽  
Nowshad Amin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Series Data ◽  
Short Term ◽  
Energy Forecasting ◽  
Artificial Neural ◽  
Hybrid Renewable Energy

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

scholarly journals Estimating the Causal Effects of Cruise Traffic on Air Pollution using Randomization-Based Inference

2021 ◽  
Author(s):  
Léo Zabrocki ◽  
Marion Leroutier ◽  
Marie-Abèle Bind
Keyword(s):  
Air Pollution ◽  
Time Series Data ◽  
Air Pollutant ◽  
Series Data ◽  
Randomized Experiments ◽  
Adverse Health Effects ◽  
Maritime Traffic ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Matched Data

Local environmental organizations and media have recently expressed concerns over air pollution induced by maritime traffic and its potential adverse health effects on the population of Mediterranean port cities. We explore this issue with unique high-frequency data from Marseille, France’s largest port for cruise ships, over the 2008- 2018 period. Using a new pair-matching algorithm designed for time series data, we create hypothetical randomized experiments and estimate the variation in air pollutant concentrations caused by a short-term increase in cruise vessel traffic. We carry out a randomization-based approach to compute 95% Fisherian intervals (FI) for constant treatment effects consistent with the matched data and the hypothetical intervention. At the hourly level, cruise vessels’ arrivals increase concentrations of nitrogen dioxide (NO2) by 4.7 μg/m³ (95% FI: [1.4, 8.0]), of sulfur dioxide (SO2) by 1.2 μg/m³ (95% FI: [-0.1, 2.5]), and of particulate matter (PM10) by 4.6 μg/m³ (95% FI: [0.9, 8.3]). At the daily level, cruise traffic increases concentrations of NO2 by 1.2 μg/m³ (95% FI: [-0.5, 3.0]) and of PM10 by 1.3 μg/m³ (95% FI: [-0.3, 3.0]). Our results suggest that well-designed hypothetical randomized experiments provide a principled approach to better understand the negative externalities of maritime traffic.

scholarly journals Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry

2020 ◽  
Vol 10 (3) ◽  
pp. 829
Author(s):  
Tomas Eloy Salais-Fierro ◽  
Jania Astrid Saucedo-Martinez ◽  
Roman Rodriguez-Aguilar ◽  
Jose Manuel Vela-Haro
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Automotive Industry ◽  
Historical Data ◽  
Series Data ◽  
Qualitative And Quantitative ◽  
Artificial Neural ◽  
Demand Forecasts

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data.

scholarly journals The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting

2016 ◽  
Vol 6 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Eren Bas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Neuron Model ◽  
Differential Evolution Algorithm ◽  
Series Data ◽  
Feed Forward ◽  
Evolution Algorithm ◽  
Artificial Neural

Abstract In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.

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