Time Series Analysis

Time series analysis is the analysis of a series of data that has been collected over time. Typically it is collected at regular intervals but it need not be. Time Series Analysis is a specific instance of Digital Series Analysis. Digital Series Analysis is the analysis of any series of data that is recorded over a defined space, an example of a space being "time" in Time Series Analysis, but another common space is physical distance.
One key characteristic of most, if not all, digital series is that neighbouring measurements are correlated. This might be so obvious that you've never thought about it technical terms before but the temperature tomorrow is likely to be similar to the temperature today for example.

In the following drop down menus you'll find links to pages that will guide you through understanding various aspects of Digital Series Analysis. Included are code cells written in python so you can re-create the plots and analysis methods.

01

Digital Series Analysis basics

1. An Introduction to Digital Series Analysis
2. The Nyquist frequency and aliasing
3. Correlations and Convolutions

02

Fourier transform and filtering

1. Fourier transforms and the frequency domain

03

Time-frequency transforms - Having your cake and eating it too?

04

Wavelets and Curvelets