Time Series Analysis in Python
Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
4.45 (2722 reviews)
18,528
students
7.5 hours
content
May 2023
last update
$79.99
regular price
What you will learn
Differentiate between time series data and cross-sectional data.
Understand the fundamental assumptions of time series data and how to take advantage of them.
Transforming a data set into a time-series.
Start coding in Python and learn how to use it for statistical analysis.
Carry out time-series analysis in Python and interpreting the results, based on the data in question.
Examine the crucial differences between related series like prices and returns.
Comprehend the need to normalize data when comparing different time series.
Encounter special types of time series like White Noise and Random Walks.
Learn about "autocorrelation" and how to account for it.
Learn about accounting for "unexpected shocks" via moving averages.
Discuss model selection in time series and the role residuals play in it.
Comprehend stationarity and how to test for its existence.
Acknowledge the notion of integration and understand when, why and how to properly use it.
Realize the importance of volatility and how we can measure it.
Forecast the future based on patterns observed in the past.
Screenshots
Related Topics
2567312
udemy ID
9/19/2019
course created date
10/1/2019
course indexed date
Bot
course submited by