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.
Martin is a trained biostatistician, programmer, consultant and data science enthusiast. His main objective: Explaining data science in a straightforward way. You can find his latest work over at: r-tutorials.com
Course Overview Welcome to Mining Data from Time Series. This is Martin Burger for Pluralsight. In this course, you will learn how to use Python and StatsModels for time series analysis and forecasting. The main models discussed in the course are ARIMA for non-seasonal and seasonal data, seasonal decomposition, as well as exponential smoothing. These three models are the standard tools for univariate time series analysis. Now to fully understand these models and to even make them better, there are some statistical concepts that you need to know. Mainly these are stationarity and autocorrelation. In the course, you will learn how to test for these statistical traits. And you will also learn how to use the new Prophet package from Facebook. By the end of this course, you will know how to use Python to implement and visualize standard time series models. Now to fully benefit from this course, you should have some basic Python skills. And, of course, it is a good idea to have Anaconda with Python 3 ready on your computer or the required add-on packages we discuss in the course. I really hope you will use this course to your advantage and practice time series analysis in Python.