Time Series Analysis in Python

5.0 /5

Time Series Analysis in Python
at
UDEMY
Overview

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

Skills you will learn

Who should do this course?

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.

What are the course deliverables?

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

More about this course

We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards. Then throughout the course, we will work with a number of Python libraries, providing you with a complete training We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima. With these tools we will master the most widely used models out there:
AR (autoregressive model)
MA (moving-average model)
ARMA (autoregressive-moving-average model)
ARIMA (autoregressive integrated moving average model)
ARIMAX (autoregressive integrated moving average model with exogenous variables)
SARIA (seasonal autoregressive moving average model)
SARIMA (seasonal autoregressive integrated moving average model)
SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
ARCH (autoregressive conditional heteroscedasticity model)