Mathematical Foundations of Time Series Analysis: A Concise Introduction - Image 1

£105.30

Springer Mathematical Foundations of Time Series Analysis: A Concise Introduction

Name: Mathematical Foundations of Time Series Analysis: A Concise Introduction
Brand: Springer
Price: 105.3 GBP
Availability: InStock

Econometrics

It has never been this cheap. We have no record of a lower price.

£105 today · cheaper than every other day in the last 5 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 48 days • 48 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 48 days • 1 price levels

Days at Price

Price Analysis

Most common price: £105 (48 days, 100.0%)

Price range: £105 - £105

Price levels: 1 different prices over 48 days

Description

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike. Review “‘This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. … It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.’ … The book can be recommended to all readers, who are interested in this field.” (Ludwig Paditz, zbMath 1414.62001, 2019) “This book is a rigorous, mathematically clear and self-contained and quite complete text on time series analysis, suitable both for graduate courses and as a reference book for researchers and users of stochastic temporal models.” (Nazaré Mendes Lopes, Mathematical Reviews, December, 2018) “Beran (Univ. of Konstanz, Germany) presents the mathematical foundations of time series analysis at a level suitable for advanced graduate students and researchers in statistics. The presentation is extremely concise … . the book gives definitions, theorems, and proofs, along with a few exercises and solutions. … it may be useful to graduate students and researchers as a reference.” (B. Borchers, Choice, Vol. 56 (03), November, 2018) From the Back Cover This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike. About the Author Jan Beran is Professor of Statistics at the Department of Mathematics and Statistics at the University of Konstanz, Germany. After completing his Ph.D. in mathematics at the ETH Zurich, Switzerland, he worked at several universities in the USA and at the University of Zurich in Switzerland. He has a broad range of interests, from long-memory processes and asymptotic theory to applications in finance, biology, and musicology.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030089754
Category: Books > Subjects > Business, Finance & Law > Economics > Econometrics
Domain: Amazon UK
Release Date: 11 December 2018
Listed Since: 12 January 2019

Barcode

No barcode data available

Springer Mathematical Foundations of Time Series Analysis: A Concise Introduction

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

The Analysis of Time Series: Theory and Practice (Monographs on Statistics and Applied Probability)

Time Series Analysis with Long Memory in View (Wiley Series in Probability and Statistics)

Empirical Economic and Financial Research: Theory, Methods and Practice: 48 (Advanced Studies in Theoretical and Applied Econometrics, 48)

Time Series Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Long-Memory Time Series: Theory and Methods (Wiley Series in Probability and Statistics)

Time Series: A Biostatistical Introduction (Oxford Statistical Science Series, No. 5)

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

Time Series: A Biostatistical Introduction (Oxford Statistical Science Series)

Elements of Nonlinear Time Series Analysis and Forecasting (Springer Series in Statistics)

Time Series Analysis

Time-Series Analysis: A Comprehensive Introduction for Social Scientists

Time Series for Economics and Finance

Dependence in Probability and Statistics: 187 (Lecture Notes in Statistics, 187)

Wiley Time Series Analysis - Probability and Statistics Book

Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)

Large Sample Inference For Long Memory Processes

Nonlinear Time Series: Theory, Methods and Applications with R Examples (Chapman & Hall/CRC Texts in Statistical Science)

Multivariate Time Series Analysis and Applications (Wiley Series in Probability and Statistics)

Time Series Analysis: Nonstationary and Noninvertible Distribution Theory: 4 (Wiley Series in Probability and Statistics)

Multivariate Time Series Analysis: With R and Financial Applications (Wiley Series in Probability and Statistics)

Introduction to Time Series Modeling with Applications in R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)