Python For Time Series Data

Machine Learning for Time-Series with Python [Repost]  eBooks & eLearning

Posted by IrGens at July 29, 2023
Machine Learning for Time-Series with Python [Repost]

Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods by Ben Auffarth
English | October 29, 2021 | ISBN: 1801819629 | True EPUB/PDF | 370 pages | 17/12.4 MB

Practical Python For Time Series Analysis & Modelling  eBooks & eLearning

Posted by ELK1nG at Aug. 14, 2024
Practical Python For Time Series Analysis & Modelling

Practical Python For Time Series Analysis & Modelling
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.26 GB | Duration: 2h 27m

A course to learn how to handle time series through practical cases, especially in the energy sector.

Practical Python For Time Series Analysis & Modelling  eBooks & eLearning

Posted by ELK1nG at Aug. 14, 2024
Practical Python For Time Series Analysis & Modelling

Practical Python For Time Series Analysis & Modelling
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.26 GB | Duration: 2h 27m

A course to learn how to handle time series through practical cases, especially in the energy sector.

XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build  eBooks & eLearning

Posted by Free butterfly at March 26, 2025
XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build

XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build, evaluate, and deploy predictive models with expert guidance by Partha Pritam Deka, Joyce Weiner
English | December 13, 2024 | ISBN: 180512305X | 308 pages | PDF | 9.98 Mb

Making Numerical Predictions for Time Series Data - Part 1/3  eBooks & eLearning

Posted by ELK1nG at May 19, 2021
Making Numerical Predictions for Time Series Data - Part 1/3

Making Numerical Predictions for Time Series Data - Part 1/3
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 35 lectures (6h 10m) | Size: 5.74 GB

Using Excel to make Numerical Predictions on Time Series data

Forecasting Time Series Data with Prophet - Second Edition  eBooks & eLearning

Posted by TiranaDok at March 31, 2024
Forecasting Time Series Data with Prophet - Second Edition

Forecasting Time Series Data with Prophet - Second Edition: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool by Greg Rafferty
English | March 31, 2023 | ISBN: 1837630410 | 282 pages | PDF | 10 Mb

Forecasting Time Series Data with Facebook Prophet  eBooks & eLearning

Posted by sammoh at March 12, 2021
Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet
English | 2020 | ISBN: 9781800568532 | 270 pages | True ( PDF, MOBI ) | 30.24 MB

Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly (repost)

Deep Learning for Time Series Cookbook:
Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

English | 2024 | ISBN: 9781805129233 | 443 Pages | PDF EPUB (True) | 14 MB
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly (repost)

Deep Learning for Time Series Cookbook:
Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

English | 2024 | ISBN: 9781805129233 | 443 Pages | PDF EPUB (True) | 14 MB

Deep Learning for Time Series Cookbook  eBooks & eLearning

Posted by hill0 at April 1, 2024
Deep Learning for Time Series Cookbook

Deep Learning for Time Series Cookbook:
Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

English | 2024 | ISBN: 1805129236 | 443 Pages | EPUB | 8.2 MB