Price prediction and forecasting for items in the online game Path of
Exile using Machine and Deep Learning methods

Τζιβάκης, Δημήτριος; Tzivakis, Dimitrios

dc.contributor.author	Τζιβάκης, Δημήτριος	el
dc.contributor.author	Tzivakis, Dimitrios	en
dc.date.accessioned	2019-05-16T10:17:12Z
dc.date.available	2019-05-16T10:17:12Z
dc.date.issued	2019-05-16
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/48750
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.16468
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Μηχανική εκμάθηση	el
dc.subject	Επαναλαμβανόμενα νευρωνικά δίκτυα	el
dc.subject	Κατηγοριοποίηση	en
dc.subject	Γραμμική παλινδρόμηση	en
dc.subject	Πρόβλεψη	en
dc.subject	Machine learning	en
dc.subject	Recurrent neural networks	en
dc.subject	Artificial neural networks	en
dc.subject	RNN	en
dc.subject	LSTM	en
dc.title	Price prediction and forecasting for items in the online game Path of Exile using Machine and Deep Learning methods	en
heal.type	bachelorThesis
heal.classification	Μηχανική εκμάθηση	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-03-13
heal.abstract	Making the most profit in an online game with the least time investment is perhaps one of the most asked questions new players have. It requires a vast amount of knowledge especially for complex games with in-game economies. Using Path of Exile as our game of choice, we have built a dataset of in-game item transactions using its built-in API and using machine learning we analyse the dataset to find ways to reduce the knowledge needed for new players to generate currency faster and more efficiently through buy and sell transactions. After downloading 6 months‘ worth of data from the API feed, we apply a pipeline in which we read the data, convert it to a readable format using a document based database - since natively the feed schema is highly nested- and use statistics and domain knowledge to engineer features which will later help our machine learning models perform better. We finally produce two datasets:  A dataset comprising of items that present a firm set of attributes through time. We use Recurrent Neural Networks in the form of LSTM to build models to try and forecast the future prices of these items. These models can later be used to help players decide where to buy or sell the items according to our forecast model.A dataset comprising of items that present a variable number of features. We use different machine learning algorithms as well as Artificial Neural Network models to predict the price of an item or classify it in range price clusters meaningful to a player. A player can later use these estimators, to get information on the price of items and decide if an item is valuable and how much.	en
heal.advisorName	Κοζύρης, Νεκτάριος	el
heal.committeeMemberName	Κοζύρης, Νεκτάριος	el
heal.committeeMemberName	Τσουμάκος, Δημήτριος	el
heal.committeeMemberName	Στάμου, Γεώργιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	109 σ.
heal.fullTextAvailability	true