Temporal networks (254 résultats)

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Paperback. Etat : Very Good. --ALL INVENTORY 40% OFF in Sept--Unmarked. Special editors: DeLiang Wang, Walter J. Freeman, Robert Kozma, Andrej G. Lozowski, and Ali A. Minai. pp953-1329. Unmarked. Charts, Graphs, etc. Topics include Auditory Processing, Associative networks, Temporal Processing, Coincidence Detection, Neural Modeling, etc. …

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Rustica (tapa blanda). Etat : New. Etat de la jaquette : Nuevo. 1. Project planning, scheduling, and control are regularly used in business and the service sector of an economy to accomplish outcomes with limited resources under critical time constraints. To aid in solving these problems, network-based planning methods have been developed that now exist in a wide variety of forms, cf. Elmaghraby (1977) and Moder et al. (1983). The so-called classical project networks, which are used in the network techniques CPM and PERT and which represent acyclic weighted directed graphs, are able to describe only projects whose evolution in time is uniquely specified in advance. Here every event of the project is realized exactly once during a single project execution and it is not possible to return to activities previously carried out (that is, no feedback is permitted). LIBRO. …

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Taschenbuch. Etat : Neu. Neuware.

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paperback. Etat : New. Paperback.Pub Date:2017-12-01 Pages:136 Language:Chinese Publisher:National University of Defense Technology Press Space-time social network is a combination of social network. space network and time network. including social network information. activity information (including spatial objects Information). user participation activity information and user trajectory information. four categories of information. covering the two research hotspots of tense-oriented social networks and geographic.…

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Taschenbuch. Etat : 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.…