# Time Series Analysis in Python 2020

Time Series Analysis in Python 2020. Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting

How does a commercial bank forecast the expected performance of their loan portfolio?

Or how does an investment manager estimate a stock portfolio’s risk?

Which are the quantitative methods used to predict real-estate properties?

If there is some time dependency, then you know it – the answer is: time series analysis.

This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.

In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice.

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.

## Time Series Analysis in Python 2020 Description

### 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.

The Complete Python Masterclass Learn Python From Scratch

### What you’ll 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.

### Who this course is for:

• 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.