9783330800342 - Forex Trend Classification by Machine Learning de Baasher, Areej A.; Fakhr, Mohamed W. (7 resultados)

Paperback. Condición: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condición: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.

Paperback. Condición: Neu. Neu Neuware, Importqualität, auf Lager - Foreign Currency Exchange market (Forex) is a highly volatile complex time series for which predicting the daily trend is a challenging problem. In this book, we investigate the prediction of the ' high ' exchange rate daily trend as classification problem (two classes), with uptrend and downtrend outcomes. Foreign Exchange (Forex) market trend was predicted using classification and machine learning techniques for the sake of gaining long-term profits. The trading strategy here is to take one action per day, where this action is either buy or sell based on the prediction we have. We view the prediction problem as a classification task, thus this work is not trying to predict the actual exchange rate value between two currencies, but rather, if that exchange rate is going to rise or fall. Forex daily exchange rate values can be seen as a time series data and all time series data forecasting and data mining techniques can be used to do the required classification task.

Taschenbuch. Condición: Neu. Forex Trend Classification by Machine Learning | Areej A. Baasher (u. a.) | Taschenbuch | 112 S. | Englisch | 2016 | Noor Publishing | EAN 9783330800342 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Foreign Currency Exchange market (Forex) is a highly volatile complex time series for which predicting the daily trend is a challenging problem. In this book, we investigate the prediction of the ' high ' exchange rate daily trend as classification problem (two classes), with uptrend and downtrend outcomes. Foreign Exchange (Forex) market trend was predicted using classification and machine learning techniques for the sake of gaining long-term profits. The trading strategy here is to take one action per day, where this action is either buy or sell based on the prediction we have. We view the prediction problem as a classification task, thus this work is not trying to predict the actual exchange rate value between two currencies, but rather, if that exchange rate is going to rise or fall. Forex daily exchange rate values can be seen as a time series data and all time series data forecasting and data mining techniques can be used to do the required classification task. 112 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Baasher Areej A.Areej A. Baasher is a Sudanese researcher studied in Egypt at Arab Academy for Science, Technology and Maritime Transport where she successfully completed two degrees: Bachelor in Computer Science 2007, Masters in Com.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Foreign Currency Exchange market (Forex) is a highly volatile complex time series for which predicting the daily trend is a challenging problem. In this book, we investigate the prediction of the ' high ' exchange rate daily trend as classification problem (two classes), with uptrend and downtrend outcomes. Foreign Exchange (Forex) market trend was predicted using classification and machine learning techniques for the sake of gaining long-term profits. The trading strategy here is to take one action per day, where this action is either buy or sell based on the prediction we have. We view the prediction problem as a classification task, thus this work is not trying to predict the actual exchange rate value between two currencies, but rather, if that exchange rate is going to rise or fall. Forex daily exchange rate values can be seen as a time series data and all time series data forecasting and data mining techniques can be used to do the required classification task.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch.