£111.01

Springer - Time Series Analysis Methods for Flight Data

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Description

Master the complexities of aviation data with this specialized text from Springer. This book provides a comprehensive look at the different facets of flight data analysis, covering essential goals, methods, and practical implementation techniques. Because mass flight data behaves as time series, this resource explains how to apply specific analysis methods to handle these unique datasets. Readers will learn how to navigate various technical processes, including data filtering, data extension, and feature optimization. The text also covers advanced applications such as similarity search, trend monitoring, fault diagnosis, and parameter prediction. By exploring these methods, you can better understand how to manage large-scale aviation information. The book even discusses the establishment of an intelligent information-processing platform designed to assist in aircraft condition monitoring and training, making it a valuable resource for those working in AI, machine learning, and aviation data science.

Key Features

Covers essential flight data analysis goals, methods, and practical implementation techniques for technical professionals.

Explains time series analysis methods specifically applied to the unique characteristics of mass flight data.

Provides detailed guidance on data filtering, data extension, and feature optimization for cleaner datasets.

Includes methods for similarity search, trend monitoring, and fault diagnosis to improve data accuracy.

Discusses parameter prediction and the use of intelligent information-processing platforms for aircraft monitoring.

Supports advanced applications in aircraft condition monitoring and training through structured data processing.

Product Specifications

Format
Paperback
Domain
Amazon UK
Release Date
07 July 2018
Listed Since
09 July 2018

Barcode

No barcode data available

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