Contents [hide]
In [105]:
picture
Out[105]:

Feature Analysis for Time Series

Authors: Isadora Nun isadoranun@g.harvard.edu , Pavlos Protopapas pavlos@seas.harvard.edu

Contributors: Daniel Acuña, Nicolás Castro, Rahul Dave, Cristobal Mackenzie, Jorge Martinez, Adam Miller, Karim Pichara, Andrés Riveros, Brandon Sim and Ming Zhu

Introduction

A time series is a sequence of observations, or data points, that is arranged based on the times of their occurrence. The hourly measurement of wind speeds in meteorology, the minute by minute recording of electrical activity along the scalp in electroencephalography, and the weekly changes of stock prices in finances are just some examples of time series, among many others.

Some of the following properties may be observed in time series data [1]:

  • the data is not generated independently
  • their dispersion varies in time
  • they are often governed by a trend and/or have cyclic components

The study and analysis of time series can have multiple ends: to gain a better understanding of the mechanism generating the data, to predict future outcomes and behaviors, to classify and characterize events, or more.

In [108]:
animation.FuncAnimation(fig, animate, init_func=init,
                        frames=100, interval=200, blit=True)
Out[108]:


Once Loop Reflect