Torrent details for "Atwan T. Time Series Analysis with Python Cookbook. Practical re…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
732.9 MB
Info Hash:
5C095F9B7E3AFF20B08B38F291561B38E69B8471
Added By:
Added:
March 21, 2026, 11:40 a.m.
Stats:
|
(Last updated: March 21, 2026, 11:41 a.m.)
| File | Size |
|---|---|
| Atwan T. Time Series Analysis with Python Cookbook. Practical recipes...2ed 2026.pdf | 165.4 MB |
| Code.zip | 567.4 MB |
Name
DL
Uploader
Size
S/L
Added
-
38.0 MB
[41
/
5]
2023-07-01
| Uploaded by indexFroggy | Size 38.0 MB | Health [ 41 /5 ] | Added 2023-07-01 |
-
732.9 MB
[73
/
24]
2026-03-21
| Uploaded by andryold1 | Size 732.9 MB | Health [ 73 /24 ] | Added 2026-03-21 |
NOTE
SOURCE: Atwan T. Time Series Analysis with Python Cookbook. Practical recipes...2ed 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format
To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples. You'll hit the ground running 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. Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python
×