Transformers & Temporal Neural Networks for Financial Time-Series
Language: English
Published by Amazon Digital Services LLC - Kdp Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Neuware - Reactive PublishingMaster the architectures driving modern quantitative finance and time-series forecasting.Transformers & Temporal Neural Networks for Financial Time-Series provides a rigorous, practical breakdown of advanced deep learning models tailored specifically for non-stationary, noisy financial data. Designed for quantitative analysts, data scientists, and financial engineers, this book bridges the gap between deep learning theory and real-world market application.Financial data presents unique challenges, concept drift, low signal-to-noise ratios, and complex temporal dependencies, that traditional econometric models often struggle to capture. This guide walks you through applying sequence-based models and attention mechanisms to overcome these obstacles.Inside, you will explore: - Temporal Modeling Fundamentals: Understand the strengths and limitations of Recurrent Neural Networks (RNNs), LSTMs, and GRUs when processing sequentially ordered market data.- Attention & Transformer Architectures: Adapt multi-head attention mechanisms, positional encoding, and specialized Transformers (such as Temporal Fusion Transformers and Informer architectures) to financial forecasting.- Feature Engineering & Preprocessing: Prepare raw financial inputs, handle non-stationarity, and construct robust features while avoiding lookahead bias.- Model Training & Evaluation: Implement specialized loss functions, backtesting frameworks, and validation techniques tailored to time-series data.- Practical Implementation: Develop reproducible code patterns for training, tuning, and evaluating models on real-world datasets.Whether you are looking to enhance predictive accuracy, model multi-horizon temporal patterns, or modernize your quantitative pipeline, this text delivers a structured, code-focused approach to deep learning in finance.…
Seller Inventory # 9798189456299
- Title
- Transformers & Temporal Neural Networks for Financial Time-Series
- Author
- James Preston
- Publisher
- Amazon Digital Services LLC - Kdp Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798189456299
- Item weight
- 644 grams
- Dimensions
- 229x152x34 mm
Master the architectures driving modern quantitative finance and time-series forecasting.
Transformers & Temporal Neural Networks for Financial Time-Series provides a rigorous, practical breakdown of advanced deep learning models tailored specifically for non-stationary, noisy financial data. Designed for quantitative analysts, data scientists, and financial engineers, this book bridges the gap between deep learning theory and real-world market application.
Financial data presents unique challenges, concept drift, low signal-to-noise ratios, and complex temporal dependencies, that traditional econometric models often struggle to capture. This guide walks you through applying sequence-based models and attention mechanisms to overcome these obstacles.
Inside, you will explore:
-
Temporal Modeling Fundamentals: Understand the strengths and limitations of Recurrent Neural Networks (RNNs), LSTMs, and GRUs when processing sequentially ordered market data.
-
Attention & Transformer Architectures: Adapt multi-head attention mechanisms, positional encoding, and specialized Transformers (such as Temporal Fusion Transformers and Informer architectures) to financial forecasting.
-
Feature Engineering & Preprocessing: Prepare raw financial inputs, handle non-stationarity, and construct robust features while avoiding lookahead bias.
-
Model Training & Evaluation: Implement specialized loss functions, backtesting frameworks, and validation techniques tailored to time-series data.
-
Practical Implementation: Develop reproducible code patterns for training, tuning, and evaluating models on real-world datasets.
Whether you are looking to enhance predictive accuracy, model multi-horizon temporal patterns, or modernize your quantitative pipeline, this text delivers a structured, code-focused approach to deep learning in finance.
"Synopsis" may belong to another edition of this title.
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