| 000 -LEADER |
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| fixed length control field |
03528cam a2200397Ii 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
jomaaum |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220106132010.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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| fixed length control field |
m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn||||mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
200826s2020 caua o 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1484259912 |
|
|---|
| International Standard Book Number |
9781484259917 |
|
|---|
| International Standard Book Number |
9781484259924 |
|
|---|
| International Standard Book Number |
1484259920 |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-1-4842-5 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA280 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.55 |
| Edition number |
23 |
| Item number |
V829 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Vishwas, B. V |
| 9 (RLIN) |
44866 |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Hands-on time series analysis with Python : |
| Remainder of title |
from basics to bleeding edge techniques / |
| Statement of responsibility, etc |
B V Vishwas, Ashish Patel |
| 260 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Berkeley, CA : |
| Name of publisher, distributor, etc |
APress, |
| Date of publication, distribution, etc |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 407 p. : |
| Other physical details |
ill.; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 1: Time Series and its Characteristics -- Chapter 2: Data Wrangling and Preparation for Time Series -- Chapter 3: Smoothing Methods -- Chapter 4: Regression Extension Techniques for Time Series -- Chapter 5: Bleeding Edge Techniques -- Chapter 6: Bleeding Edge Techniques for Univariate Time Series -- Chapter 7: Bleeding Edge Techniques for Multivariate Time Series -- Chapter 8: Prophet |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Available to OhioLINK libraries |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples. The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing, and ways of crafting the features through data wrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework like StatsModels, pmdarima. Further, Book explains the building classification models using sktime, and covers how to leverage advance deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It finally concludes by explaining the popular framework fbprophet for modeling time series analysis. After completion of the book, the reader will have a good understanding of working with different techniques of time series methods. All the codes presented in this notebook are available in Jupyter notebooks, which allows readers to do hands-on and enhance them in exciting ways. What You'll Learn Explains basics to advanced concepts of time series How to design, develop, train, and validate time-series methodologies What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. Univariate and multivariate problem solving using fbprophet |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Time-series analysis |
| General subdivision |
Data processing. |
| 9 (RLIN) |
44867 |
|
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 9 (RLIN) |
4569 |
| 655 #4 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Patel, Ash. |
| 9 (RLIN) |
44868 |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Ohio Library and Information Network. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Qualifying information |
Original |
| International Standard Book Number |
1484259912 |
| -- |
9781484259917 |
| Record control number |
(OCoLC)1148885006 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
OhioLINK |
| Public note |
Connect to resource |
| Uniform Resource Identifier |
http://rave.ohiolink.edu/ebooks/ebc/9781484259924 |
|
|---|
| Materials specified |
SpringerLink |
| Public note |
Connect to resource |
| Uniform Resource Identifier |
http://link.springer.com/10.1007/978-1-4842-5992-4 |
|
|---|
| Materials specified |
SpringerLink |
| Public note |
Connect to resource (off-campus) |
| Uniform Resource Identifier |
http://proxy.ohiolink.edu:9099/login?url=http://link.springer.com/10.1007/978-1-4842-5992-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Book |
