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Author : Loren D. Enochson
Publisher :
ISBN 13 :
Total Pages : 300 pages
Book Rating : 4.94/5 ( download)
Download or read book Programming and Analysis for Digital Time Series Data written by Loren D. Enochson and published by . This book was released on 1969 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Loren D. Enochson
Publisher :
ISBN 13 :
Total Pages : 296 pages
Book Rating : 4.02/5 ( download)
Download or read book Programming and Analysis for Digital Time Series Data written by Loren D. Enochson and published by . This book was released on 1969 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Loren D. Enochson
Publisher :
ISBN 13 :
Total Pages : 277 pages
Book Rating : 4.92/5 ( download)
Download or read book Programming and Analysis for Digital Time Series Data written by Loren D. Enochson and published by . This book was released on 1968 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents: Preprocessing of data; Digital filtering; Fourier series and Fourier transform computations; Correlation function computations; Spectral density function computations; Frequency response function and coherence function computations; Probability density function computations; Nonstationary processes; and Test case and examples.
Author : Robert K. Otnes
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 488 pages
Book Rating : 4.50/5 ( download)
Download or read book Digital Time Series Analysis written by Robert K. Otnes and published by Wiley-Interscience. This book was released on 1972 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Preliminary concepts -- Preprocessing of data -- Recursive digital filtering -- Fourier series and Fourier transform computations -- General considerations in computing power spectral density -- Correlation function and Blackman-Tukey spectrum computations -- Power and cross spectra from fast Fourier transforms -- Filter methods for the power spectral density -- Transfer function and coherence function computations -- Probability density function computations -- Miscellaneous techniques -- Test case and examples.
Author :
Publisher :
ISBN 13 :
Total Pages : 456 pages
Book Rating : 4.91/5 ( download)
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author : United States. Superintendent of Documents
Publisher :
ISBN 13 :
Total Pages : 1320 pages
Book Rating : 4.36/5 ( download)
Download or read book Monthly Catalog of United States Government Publications written by United States. Superintendent of Documents and published by . This book was released on 1966 with total page 1320 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : United States. Superintendent of Documents
Publisher :
ISBN 13 :
Total Pages : 1348 pages
Book Rating : 4.04/5 ( download)
Download or read book Monthly Catalog of United States Government Publications, Cumulative Index written by United States. Superintendent of Documents and published by . This book was released on 1970 with total page 1348 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Changquan Huang
Publisher : Springer Nature
ISBN 13 : 3031135849
Total Pages : 377 pages
Book Rating : 4.42/5 ( download)
Download or read book Applied Time Series Analysis and Forecasting with Python written by Changquan Huang and published by Springer Nature. This book was released on 2022-10-19 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Author : Dr. Avishek Pal
Publisher : Packt Publishing Ltd
ISBN 13 : 178829419X
Total Pages : 238 pages
Book Rating : 4.95/5 ( download)
Download or read book Practical Time Series Analysis written by Dr. Avishek Pal and published by Packt Publishing Ltd. This book was released on 2017-09-28 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.
Author : Bob Mather
Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.02/5 ( download)
Download or read book Time Series with Python written by Bob Mather and published by . This book was released on 2020-04-10 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks? This book is your answer. Update: Thanks for the overwhelming positive feedback. I am glad that you have found the information in this book useful. To address a couple of complaints, we have fixed a few issues with paperback formatting that were mentioned. Also, we have to mention that we have had to add links to external datasets in the book for our examples. If we added 1000's of sample data points in the book without a link, it would be take up 100's of pages, and would take away from the relevance of the code. Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python. This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code. Here's What's Included In this Book: What is a Time Series? 4 Different Elements of a Time Series Why Python is the best way to Implement Time Series Step by Step Guide to Installing Python and Importing Time Series Data 6 Different Techniques to Analyze Time Series Data 3 Advanced Time Series Concepts for Time Series Prediction Time Series Visualization Techniques in Python Even if you've never implemented Time Series before, you will still find this book useful. Scroll up and Click Buy Now
