Demand Prediction Using Machine Learning Methods and Stacked Generalization

Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.Comment: Proceedings of the 6th International Conference on Data Science, Technology and Application

Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

Group method of data handling to predict scour depth around vertical piles under regular waves

AbstractThis paper presents a new application of the Group Method Of Data Handling (GMDH), to predict pile scour depth exposed to waves. The GMDH network was developed using the Levenberg–Marquardt (LM) method in the training stage for scour prediction. Scour depth due to regular waves was modeled as a function of five dimensionless parameters, including pile Reynolds number, grain Reynolds number, sediment number, Keulegan–Carpenter number, and shields parameter. The testing results of the GMDH-LM were compared with those obtained using the Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function-Neural Network (RBF-NN), and empirical equations. In particular, the GMDH-LM provided the most accurate prediction of scour depth compared to other models. Also, the Keulegan–Carpenter number has been determined as the most effective parameter on scour depth through a sensitivity analysis. The GMDH-LM was utilized successfully to investigate the influence of the pile cross section and Keulegan–Carpenter number on scour depth

Neural Network Associative Forecasting of Demand for Goods

This article discusses the applicability of recurrent neural networks with controlled elements to the problem of forecasting market demand for goods on the four month horizon. Two variants of forecasting are considered. In the first variant, time series are used to train the neural network, including the real demand values, as well as pre-order values for 1, 2 and 3 months ahead. In the second variant, there is an iterative forecasting method. It predicts the de-mand for the next month at each step, and the training set is supplemented by the values predicted for the previous months. It is shown that the proposed methods can give a sufficiently high result. At the same time, the second ap-proach demonstrates greater potential

Neural Network Associative Forecasting of Demand for Goods

This article discusses the applicability of recurrent neural networks with controlled elements to the problem of forecasting market demand for goods on the four month horizon. Two variants of forecasting are considered. In the first variant, time series are used to train the neural network, including the real demand values, as well as pre-order values for 1, 2 and 3 months ahead. In the second variant, there is an iterative forecasting method. It predicts the de-mand for the next month at each step, and the training set is supplemented by the values predicted for the previous months. It is shown that the proposed methods can give a sufficiently high result. At the same time, the second ap-proach demonstrates greater potential

Forecasting of Turkey's Sectoral Energy Demand by Using Fuzzy Grey Regression Model

Population growth, technological developments, economical growth and efforts to achieve a high standard of living increase the demand for energy. Satisfying this increasing demand without interruption is of vital importance for countries to ensure security of supply. Safely forecasting the energy demand of Turkey, which is about 3-4 times the world average, is important for sustainable development and improving standards of living in the country. This study seeks to forecast Turkey's total energy demand and determine the distribution of this demand among sectors and the amount of unutilized energy. In the study, the energy demand projection until 2023 was revealed with fuzzy grey regression model (FGRM) using the data between years 1990-2012. Keywords: fuzzy grey prediction; sectorial energy demand in Turkey; fuzzy grey regression model JEL Classifications: C610, L69

Energy Demand Prediction: A Partial Information Game Approach

International audienceThis article proposes an original approach to predict the electric vehicles (EVs)' energy demand in a charge station using a regret minimization learning approach. The problem is modelled as a two players game involving: on the one hand the EV drivers, whose demand is unknown and, on the other hand, the service provider who owns the charge station and wants to make the best predictions in order to minimize his regret. The information in the game is partial. Indeed, the service provider never observes the EV drivers' energy demand. The only information he has access to is contained in a feedback function which depends on his predictions accuracy and on the EV drivers' consumption level. The local/expanded accuracy and the ability for uncertainty handling of the regret minimization learning approach is evaluated by comparison with three well-known learning approaches: (i) Neural Network, (ii) Support Vector Machine, (iii) AutoRegressive Integrated Moving Average process, using as benchmarks two data bases: an artificial one generated using a bayesian network and real domestic household electricity consumption data in southern California. We observe that over real data, regret minimization algorithms clearly outperform the other learning approaches. The efficiency of these methods open the door to a wide class of game theory applications dealing with collaborative learning, information sharing and manipulation

New input identification and artificial intelligence based techniques for load prediction in commercial building

The accuracy of prediction models for electrical loads are important as the predicted result can affect processes related to energy management such as maintenance planning, decision-making processes, as well as cost and energy savings. The studies on improving load prediction accuracy using Least Squares Support Vector Machine (LSSVM) are widely carried out by optimizing the LSSVM hyper-parameter which includes the Kernel parameter and the regularization parameter. However, studies on the effects of input data determination for the LSSVM have not widely tested by researchers. This research developed an input selection technique using Modified Group Method of Data Handling (MGMDH) to improve the accuracy of buildings load forecasting. In addition, a new cascaded Group Method of Data Handing (GMDH) and LSSVM (GMDH-LSSVM) model is developed for electrical load prediction to improve the prediction accuracy of LSSVM model. To further improve the prediction model ability, a Modified GMDH has been cascaded to the LSSVM model to enhance the accuracy of building electrical load prediction and reduce the complexity of GMDH model. The proposed models are compared with GMDH model, LSSVM model and Artificial Neural Network (ANN) model to observe the prediction performance. The performances of prediction for each tested models are evaluated using the Mean Absolute Percentage Error (MAPE). In this analysis, the proposed prediction model, gives 33.82% improvement of prediction accuracy as compared to LSSVM model. From this research, it can be concluded that cascading the models can improve the prediction accuracy and the proposed models can be used to predict building electrical loads

Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

Diferansiyel polinom sinir ağı tekniği ile elektrik tüketim tahmini

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Elektrik enerjisi ülkelerin refah ve çağdaşlaşma seviyesine ayna tutan metalardan bir tanesidir. Elektrik enerjisi depolayamayan üretildiği anda tüketilmesi gereken enerji türüdür. Bu nedenle arzın karşılanması için tüketim talebinin tahmin doğruluğu önem arz etmektedir. Nüfus artışının hızlanması, gelişen teknoloji ve hızlı sanayileşme gibi gelişmelerin yaşanması ile elektrik enerjisinin kullanımı ve ihtiyacı her geçen gün artmaktadır. Ayrıca elektrik enerjisi, ülkeler arası rekabetin önemli unsurları arasında yer alması nedeniyle de ülkeler doğru tahmin sistemi geliştirerek daha doğru tahminler elde etme üzerine çalışmalar yapmaktadır. Bu çalışmada, uygulama yaygınlığı ile öne çıkan yapay sinir ağları ve yapay zeka tekniklerinin yeni bir çeşidi olan diferansiyel polinom sinir ağları ile Türkiye'nin elektrik enerji talebi tahmin edilmiştir. İhracat, ithalat, nüfus, kurulu güç ve gayri safi yurtiçi hasıla elektrik tüketimini etkileyen önemli faktörlerdir. Bu nedenle Türkiye'nin elektrik enerjisi tüketim tahmininde 1965-2016 yılları için bağımsız değişken olarak ele alınarak model girdileri olarak kullanılmıştır. Her iki metotla elde edilen tahmin sonuçlarının performansı karşılaştırmalı olarak ortaya konulmuştur. Karşılaştırmalar neticesinde diferansiyel polinom sinir ağı yönteminden elde edilen sonuçların ortalama mutlak yüzde hatası %4,32 daha düşük elde edilmiştir. Sonuçların analizinde kullanılan istatistiksel yöntemler diferansiyel polinom sinir ağının yüksek doğrulukta tahminler gerçekleştirdiği anlaşılmıştır. Anahtar kelimeler: Diferansiyel polinom sinir ağları, Yapay sinir ağları, Elektrik enerjisi, tüketim tahminiElectricity energy is one of the commodities in terms that mirrors the level of welfare and modernization of the countries. Electric energy is the type of energy that must be consumed when it is produced without storing it. For this reason, the prediction accuracy of the demand for consumption is important to meet the supply. The use of electrical energy and the need for it is increasing day by day with the developments such as the acceleration of population growth, developing technology and rapid industrialization. In addition, as electricity energy is one of the important elements of competition among countries, countries are working on obtaining more accurate estimates by developing correct estimation system. In this study, Turkey's electric energy consumption has been estimated with the artificial neural networks technique, which are prominent in application prevalence and differential polynomial neural networks which a new kind of neural networks technique. Export, import, population, installed power and gross domestic product are important factors affecting electricity consumption. Therefore, by considering as independent variables were used as inputs for the 1965-2016 model years in Turkey's electricity consumption estimated.As a result of the comparisons, the mean absolute percentage error of the results obtained from the differential polynomial neural network method was 4.32% lower. The statistical methods used in the analysis of the results have revealed that the differential polynomial neural network performed highly accurate estimates. Keywords: Differential polynomial neural networks, artificial neural networks, electric energy, consumption forecas