From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.
In this chapter you'll be introduced to the ideas of correlation and autocorrelation for time series. Correlation describes the relationship between two time series and autocorrelation describes the relationship of a time series with its past values.
In this chapter you'll learn about some simple time series models. These include white noise and a random walk.
In this chapter you'll learn about autoregressive, or AR, models for time series. These models use past values of the series to predict the current value.
In this chapter you'll learn about another kind of model, the moving average, or MA, model. You will also see how to combine AR and MA models into a powerful ARMA model.
This chapter will show you how to model two series jointly using cointegration models. Then you'll wrap up with a case study where you look at a time series of temperature data from New York City.