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 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 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 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.

scholarly journals Volatility forecast with artificial neural networks as univariate time series, with examples from stock market indexes

2018 ◽  
Vol 7 (3) ◽  
pp. 291-299
Author(s):  
Fahriye Uysal ◽  
Burak Erturan
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Financial Markets ◽  
Global Economy ◽  
Historical Data ◽  
Univariate Time Series ◽  
Artificial Neural ◽  
Market Indexes

The tools that are offered to investors in financial markets are fluctuating. As this fluctuation causes losses as well as earnings, it is characterised as a risk for the investor. Especially, fluctuations that may occur in globally important markets and financial instruments have great significance, not just for investor but also for the global economy. Volatility, as a measure of fluctuations taking place in markets, is often used particularly by investors and all economic actors. Therefore, in recent years, future volatility predictions have gained importance. The aim of this research is forecasting future volatility values using the historical data of S&P 500, FTSE 100 and NIKKEI 225 stock market indexes. The progress of historical volatility values in years is presented and generated univariate time series is modelled with artificial neural networks. Future forecasts are done with the obtained model and results are interpreted. Keywords: Artificial neural networks, volatility, time series analysis, stock market indexes.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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