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Mining Data from Time Series

Focused View

Martin Burger

2:58:51

43 View
  • 01 - Course Overview.mp4
    01:36
  • 02 - Intro.mp4
    01:15
  • 03 - Managing Expectations.mp4
    02:43
  • 04 - What Is Time Series Analysis.mp4
    06:52
  • 05 - Forecast Time Frames.mp4
    05:37
  • 06 - Why Use Python.mp4
    02:57
  • 07 - Course Datasets.mp4
    05:47
  • 08 - Time Series Data Objects.mp4
    04:10
  • 09 - Summary.mp4
    01:12
  • 10 - Intro.mp4
    01:59
  • 11 - Time Series Visualizations.mp4
    06:51
  • 12 - Stationarity in Time Series Data.mp4
    04:05
  • 13 - ADF Test for Stationarity.mp4
    05:15
  • 14 - Autocorrelation in Time Series Variables.mp4
    04:06
  • 15 - ACF and PACF Plots for Autocorrelation.mp4
    04:24
  • 16 - Moving Averages and Smoothers.mp4
    03:30
  • 17 - Smoother Implementation.mp4
    07:44
  • 18 - Summary.mp4
    02:01
  • 19 - Intro.mp4
    01:46
  • 20 - ARIMA Background.mp4
    05:40
  • 21 - Using a Tuple.mp4
    03:37
  • 22 - Manual Parameter Selection.mp4
    04:40
  • 23 - Model Residuals.mp4
    07:27
  • 24 - Model Improvement.mp4
    05:27
  • 25 - Forecasting Based on ARIMA.mp4
    04:12
  • 26 - Summary.mp4
    01:30
  • 27 - Intro.mp4
    01:26
  • 28 - Visualizing Seasonal Data.mp4
    06:48
  • 29 - SARIMA Automation.mp4
    05:49
  • 30 - SARIMA Demo.mp4
    03:47
  • 31 - Seasonal Decomposition.mp4
    06:01
  • 32 - STL Decomposition.mp4
    08:27
  • 33 - Summary.mp4
    01:23
  • 34 - Intro.mp4
    01:23
  • 35 - Exponential Smoothing Background.mp4
    08:23
  • 36 - Demo - Exponential Smoothing.mp4
    07:26
  • 37 - Summary.mp4
    01:31
  • 38 - Intro.mp4
    01:02
  • 39 - Multivariate Time Series.mp4
    03:04
  • 40 - Background on Prophet.mp4
    05:09
  • 41 - Demo - Prophet.mp4
    05:12
  • 42 - Further Resources.mp4
    03:11
  • 43 - Course Summary.mp4
    02:26
  • Description


    Master time series analysis in Python and be able to produce powerful quantitative forecasts.

    What You'll Learn?


      Are you struggling with the analysis of time series data or do you want to create a powerful quantitative forecasting model in Python? In this course, Mining Data from Time Series, you will gain the ability to model and forecast time series in Python. First, you will learn about time series data, which is data captured along a timeline with specific statistical traits crucial for any model. Then, you will see the statistical foundations first before diving into the classic time series models of ARIMA, seasonal decomposition as well as exponential smoothing. Finally, you will explore some advanced concepts like the new Prophet package from Facebook or multivariate time series. When you are finished with this course, you will have the skills and knowledge of time series analysis needed to model and forecast standard univariate time series data sets.

    More details


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    Martin Burger
    Martin Burger
    Instructor's Courses
    Martin studied biostatistics and worked for several pharmaceutical companies before he became a data science consultant and author. He published over 15 courses on R, Tableau 9 and other data science related subjects. His main focus lies on analytics software like R and SPSS but he is also interested in modern data visualization tools like Tableau. If he is not busy coding, blogging or working out new teaching concepts you may find him skiing or hiking in the Alps.
    Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.
    • language english
    • Training sessions 43
    • duration 2:58:51
    • level advanced
    • Release Date 2023/10/12