Machine Learning for Financial Time Series - 8 Angebote vergleichen
Preise | 2013 | 2014 | 2015 | 2017 | 2019 |
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Schnitt | Fr. 38.58 (€ 39.33)¹ | Fr. 35.32 (€ 36.00)¹ | Fr. 35.32 (€ 36.00)¹ | Fr. 155.50 (€ 158.49)¹ | Fr. 35.25 (€ 35.93)¹ |
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Machine Learning for Financial Time Series (2012)
ISBN: 9783639402308 bzw. 3639402308, vermutlich in Englisch, AV Akademikerverlag, Taschenbuch, neu.
Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set. In-sample the genetic algorithm was able to successfully find prediction rules allowing for the creation of successful, feasible and robust trading strategies with high performance. In order to justify the in-sample performance, the prediction patterns were tested on an out-of -sample time period : the results confirmed the ability of the genetic algorithm to find prediction patterns with robust performance. The proposed framework shows great potential when working with high dimensional search spaces and in finding non-linear dependencies in financial data. 05.2012, Taschenbuch.
Machine Learning for Financial Time Series - Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm
ISBN: 9783639402308 bzw. 3639402308, vermutlich in Englisch, AV Akademikerverlag, Taschenbuch, neu.
Machine Learning for Financial Time Series: This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set. In-sample the genetic algorithm was able to successfully find prediction rules allowing for the creation of successful, feasible and robust trading strategies with high performance. In order to justify the in-sample performance, the prediction patterns were tested on an out of sample time period : the results confirmed the ability of the genetic algorithm to find prediction patterns with robust performance. The proposed framework shows great potential when working with high dimensional search spaces and in finding non-linear dependencies in financial data. Englisch, Taschenbuch.
Machine Learning for Financial Time Series
ISBN: 9783639402308 bzw. 3639402308, vermutlich in Englisch, VDM Verlag Dr. Müller, Saarbrücken, Deutschland, neu, Hörbuch.
This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set. In-sample the genetic algorithm was able to successfully find prediction rules allowing for the creation of successful, feasible and robust trading strategies with high performance. In order to justify the in-sample performance, the prediction patterns were tested on an out of sample time period : the results confirmed the ability of the genetic algorithm to find prediction patterns with robust performance. The proposed framework shows great potential when working with high dimensional search spaces and in finding non-linear dependencies in financial data.
Machine Learning for Financial Time Series: Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm (2012)
ISBN: 9783639402308 bzw. 3639402308, in Englisch, 76 Seiten, AV Akademikerverlag, Taschenbuch, neu.
Von Händler/Antiquariat, Amazon.com.
This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set. In-sample the genetic algorithm was able to successfully find prediction rules allowing for the creation of successful, feasible and robust trading strategies with high performance. In order to justify the in-sample performance, the prediction patterns were tested on an out—of —sample time period : the results confirmed the ability of the genetic algorithm to find prediction patterns with robust performance. The proposed framework shows great potential when working with high dimensional search spaces and in finding non-linear dependencies in financial data. Paperback, التسمية: AV Akademikerverlag, AV Akademikerverlag, مجموعة المنتجات: Book, ونشرت: 2012-05-08, ستوديو: AV Akademikerverlag, رتبة المبيعات: 5801424.
Machine Learning for Financial Time Series
ISBN: 3639402308 bzw. 9783639402308, vermutlich in Englisch, AV Akademikerverlag, Taschenbuch, neu.
Machine Learning for Financial Time Series (2012)
ISBN: 9783639402308 bzw. 3639402308, vermutlich in Englisch, VDM Verlag Dr. Müller, Saarbrücken, Deutschland, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Machine Learning for Financial (2012)
ISBN: 9783639402308 bzw. 3639402308, vermutlich in Englisch, VDM Verlag Dr. Müller, Saarbrücken, Deutschland, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Machine Learning for Financial Time Series: Identifying Prediction Patterns in Financial Time Series Using a Genetic Algorithm (2012)
ISBN: 9783639402308 bzw. 3639402308, in Englisch, 76 Seiten, AV Akademikerverlag, Taschenbuch, neu.
Von Händler/Antiquariat, Amazon.co.uk.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen