Python for Time Series Data Analysis

Python for Time Series Data Analysis



.MP4, AVC, 200 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 95 lectures 15:21:14

英语无字

https://www.udemy.com/course/python-for-time-series-data-analysis/

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Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!



Pandas for Data Manipulation



Pandas for Data Visualization



Use Statsmodels to Analyze Time Series Data



Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!



What you'll learn



Pandas for Data Manipulation



NumPy and Python for Numerical Processing



Pandas for Data Visualization



How to Work with Time Series Data with Pandas



Use Statsmodels to Analyze Time Series Data



Use Facebook's Prophet Library for forecasting



Understand advanced ARIMA models for Forecasting



Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!



This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.



We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.



Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.



Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.



Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.



This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.



So what are you waiting for! Learn how to work with your time series data and forecast the future!Python for Time Series Data Analysis



.MP4, AVC, 200 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 15h 4m | 1.61 GB



Instructor: Jose Portilla



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Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!



Pandas for Data Manipulation



Pandas for Data Visualization



Use Statsmodels to Analyze Time Series Data



Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!



What you'll learn



Pandas for Data Manipulation



NumPy and Python for Numerical Processing



Pandas for Data Visualization



How to Work with Time Series Data with Pandas



Use Statsmodels to Analyze Time Series Data



Use Facebook's Prophet Library for forecasting



Understand advanced ARIMA models for Forecasting



Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!



This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.



We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.



Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.



Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.



Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.



This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.



So what are you waiting for! Learn how to work with your time series data and forecast the future!



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Introduction



3 lectures 13:50



Course Set Up and Install



1 lecture 15:54



NumPy



7 lectures 47:04



Pandas Overview



10 lectures 01:26:36



Data Visualization with Pandas



5 lectures 42:06



Time Series with Pandas



12 lectures 01:41:17



Time Series Analysis with Statsmodels



10 lectures 01:28:54



General Forecasting Models



28 lectures 05:46:18



Deep Learning for Time Series Forecasting



13 lectures 02:12:39



Facebook’s Prophet Library



5 lectures 46:25



BONUS SECTION: THANK YOU!



1 lecture 00:10