Applied Time Series Analysis and Forecasting with Python

- 1. Time Series Concepts and Python. - 2. Exploratory Time Series Data Analysis. - 3. Stationary Time Series Models. - 4. ARMA and ARIMA Modeling and Forecasting. - 5. Nonstationary Time Series Models. - 6. Financial Time Series and Related Models. - 7. Multivariate Time Series Analysis. - 8. State Space Models and Markov Switching Models. - 9. Nonstationarity and Cointegrations. - 10. Modern Machine Learning Methods for Time Series Analysis.

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Editie: Hardcover

Nr. pagini: 372

Cod: BRT9783031135835

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Applied Time Series Analysis and Forecasting with Python

De (autor): Changquan Huang

Applied Time Series Analysis and Forecasting with Python

De (autor): Changquan Huang

Descrierea produsului

De pe acelasi raft

Applied Time Series Analysis and Forecasting with Python - Changquan Huang

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Tarek A. Atwan

Analysis of Time Series - Chris Chatfield

Python for Finance Cookbook - Second Edition: Over 80 powerful recipes for effective financial data analysis - Eryk Lewinson

Time Series Analysis and Its Applications

Time Series Analysis and Forecasting using Python & R - Jeffrey Strickland

Time Series: Modeling, Computation, and Inference, Second Edition - Raquel Prado

Econometrics and Data Science: Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems - Tshepo Chris Nokeri

Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning - Manu Joseph

Mastering Python for Finance - Second Edition: Implement advanced state-of-the-art financial statistical applications using Python - James Ma Weiming

Advanced Forecasting with Python: With State-Of-The-Art-Models Including Lstms, Facebook's Prophet, and Amazon's Deepar - Joos Korstanje

Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Ben Auffarth

Extending Power BI with Python and R: Ingest, transform, enrich, and visualize data using the power of analytical languages - Luca Zavarella

Modern Statistics: A Computer-Based Approach with Python - Ron S. Kenett

Bayesian Methods for Hackers - Cameron Davidson-Pilon

Machine Learning: Guide for Beginners with R/Python/Scala - Farth

Forecasting Time Series Data with Prophet - Second Edition: Build, improve, and optimize time series forecasting models using Meta's advanced forecast - Greg Rafferty

Machine Learning for Time Series Forecasting with Python - Francesca Lazzeri

Applied Multivariate Analysis - S. James Press

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