In this chapter, we will take a look at time series analysis and learn several ways of observing and capturing an event at different points in time. We will introduce the concept of white noise and learn about its detection in a series.
We will take the time series data and compute the differences between the consecutive observations, which will lead to the formation of a new series. These concepts will help us deep dive into time series analysis and help us build a deeper understanding around it.
In this chapter, we will cover the following topics:
- Introduction to time series analysis
- White noise
- Random walk
- Autoregression
- Autocorrelation
- Stationarity
- Differencing
- AR model
- Moving average model
- Autoregressive integrated moving average
- Optimization of parameters
- Anomaly detection