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Inhaltsangabe
Reactive Publishing
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.
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Transformers & Temporal Neural Networks for Financial Time-Series
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798189456299
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Transformers & Temporal Neural Networks for Financial Time-Series
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Taschenbuch. Zustand: Neu. 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. Bestandsnummer des Verkäufers 9798189456299
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