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Springer Time Series Analysis for Flight Data Book

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Description

Explore advanced methodologies for flight data analysis in this comprehensive guide from Springer. This book addresses the unique characteristics of mass flight data by applying time series analysis to various technical challenges. Readers will learn about essential implementation techniques including data filtering, data extension, and feature optimization. Beyond basic methods, the text covers practical applications such as similarity search, trend monitoring, fault diagnosis, and parameter prediction. The content also introduces an intelligent information-processing platform designed to assist with aircraft condition monitoring and training. This resource is built for professionals and students seeking to understand how time series methods can be applied to complex aviation datasets.

Key Features

This Springer publication covers multiple facets of flight data analysis including basic goals and implementation techniques.

Learn to apply time series analysis methods to mass flight data for tasks like data filtering and data extension.

The text provides detailed methods for feature optimization, similarity search, and trend monitoring in flight datasets.

Gain insights into fault diagnosis and parameter prediction through specialized time series analysis applications.

Access information on an intelligent information-processing platform established for aircraft condition monitoring.

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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