000			02098nam a22002417a 4500
005			20260610160115.0
008			260610s2026 |||||||| |||| 00| 0 eng d
020			_a9781805124283
040			_cPK-LaUMT
082			_a519.54 _bATW-T
100	1		_aAtwan, Tarek A. _912702
245	1	0	_aTime series analysis with Python cookbook : _bpractical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection / _cTarek A. Atwan
250			_a2nd ed.
260			_aBirmingham : _bPackt Publishing, _c2026
300			_axvii, 790 p.
490			_aExpert insight
500			_aIndex present
520			_aTo 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			_aEng
650			_aTime-series analysis _99957
650			_aPython (Computer program language) _93584
942			_cBK
999			_c141119 _d141119