Forecasting with artificial neural networks:

Zhang, Guoqiang; Patuwo, B. Eddy; Hu, Michael Y.

doi:10.1016/s0169-2070(97)00044-7

Cited by 3,547 publications

(1,947 citation statements)

References 169 publications

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“…One major application area of ANNs is forecasting for both researchers and practitioners [4]. The neural network approach produces better classification, handles complex underlying relationships better, and is stronger at interpolation [5].…”

Section: Neural Networkmentioning

confidence: 99%

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Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Penpece¹,

Elma²

2014

IJTEF

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Abstract-Forecasting sales quantity and sales revenue is very vital for a company to take action for the next period for sustainable competition. It is especially important for growing industries like grocery retailing industry. Turkey's grocery retailing industry is evolving rapidly. Due to increasing importance; the aim of this study is to forecast the sales revenue of grocery retailing industry in Turkey with the help of grocery retailers marketing costs, gross profit, and its competitors' gross profit by using artificial neural network. Artificial neural networks are models which are used for forecasting because of their capabilities of pattern recognition and machine learning. ANN method is used to forecast the sales revenue of upcoming period. According to results there are high similarities between forecasted and actual data. Forecasted results of this study are bigger or smaller than the actual data for only 10%. Because of this high accuracy, companies at grocery retailing industry in Turkey can use ANN as a forecasting tool.Index Terms-Sales revenue, forecasting sales revenue, grocery retailing industry, artificial neural network.

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Section: Neural Networkmentioning

confidence: 99%

“…It is hard for a researcher to be aware of all the work done to date in the area [4]. ANNs have increasingly been used in many disciplines from the early 1980s to recent years such as engineering, medical diagnosis, data mining, and corporate business available [6].…”

Section: Neural Networkmentioning

confidence: 99%

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Penpece¹,

Elma²

2014

IJTEF

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“…Thus ANN can be conveniently used for problems whose solutions require knowledge that is hard to specify. However to obtain maximum accuracy from the developed model the data set has to be sufficiently large (Zhang et al, 1998).The solutions of ANN are location specific and it cannot be transferable to the other locations due to location-specific conditions.…”

Section: Network Developmentmentioning

confidence: 99%

Travel Time Prediction Modelling in Mixed Traffic Conditions

2018

IJTTE

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Abstract:Over the past few decades, exponential increase in vehicle ownership resulted in issues of traffic control and management. Intelligent Transportation System (ITS) is one of the solutions for the intelligent management of traffic. ITS applications like Advanced Traveler Information Systems and Advanced Traffic Management Systems need travel time as a major input. The estimation of travel time in urban network became more complicated because of the rapid change in the system and traffic. This study is done in order to assess the impact of different travel modes on travel time. Data has been collected on a stretch of 14 km length in Warangal city, India using GPS probe vehicle along with video camera. Different private modes of transportation such as 2 wheeler, passenger car and 3 wheeler have been used as test vehicles for the collection of data in different traffic flow scenarios. Artificial Neural Network and a multi linear regression model have been developed to compare the estimated travel times with the field data. Two combinations of ANN model using single hidden layer, different numbers of neurons and epochs have been compared. The travel time of different modes has been compared and the effect of vehicle composition on travel time has been analyzed. The ANN model perform better than the regression model.

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“…7 Some of the important studies that fall into this category include [47] which combines evolutionary algorithms and fuzzy logic for predicting hourly peaks from one to seven days ahead in time [47], wavelet transformation techniques to accurately forecast power consumptions about 4 hours ahead of time [2] and short-term predictions using exponential smoothing methods [37]. Further, other machine learning techniques have been applied such as non-linear curve-fitting [48,36] and Support Vector Machines (SVM) [23,19]. It should be noted here that the load forecasting is significantly different from peak prediction [30] [37].…”

Section: Internal Structurementioning

confidence: 99%

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

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Forecasting with artificial neural networks:

Cited by 3,547 publications

References 169 publications

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey

Travel Time Prediction Modelling in Mixed Traffic Conditions

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

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