Time series analysis with Python cookbook : (Record no. 141119)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02098nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260610160115.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260610s2026 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781805124283 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.54 |
| Item number | ATW-T |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Atwan, Tarek A. |
| 245 10 - TITLE STATEMENT | |
| Title | Time series analysis with Python cookbook : |
| Remainder of title | practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection / |
| Statement of responsibility, etc | Tarek A. Atwan |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Birmingham : |
| Name of publisher, distributor, etc | Packt Publishing, |
| Date of publication, distribution, etc | 2026 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvii, 790 p. |
| 490 ## - SERIES STATEMENT | |
| Series statement | Expert insight |
| 500 ## - GENERAL NOTE | |
| General note | Index present |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Time-series analysis |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-06-10 | 519.54 ATW-T | 153074 | 2026-06-10 | 2026-06-10 | Books |