Time Series Analysis in Python

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting

Created by: 365 Careers
Creating opportunities for Data Science and Finance students

What Will I 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.

Requirements

• No prior experience with time-series is required.
• You'll need to install Anaconda. We will show you how to do that step by step.
• Some general understanding of coding languages is preferred, but not required.

Target audience

• Aspiring data scientists.
• Programming beginners.
• People interested in quantitative finance.
• Programmers who want to specialize in finance.
• Finance graduates and professionals who need to better apply their knowledge in Python.