There are 2 posts tagged moving average (this is page 1 of 1).
This tutorial will teach you how you can extract valuable information from time series, such as your sold copies on Steam or your Google Analytics. The previous part of this series introduced a technique called moving average, which has been used to attenuate the effects of noise in a signal. When signals represent an event that evolves over time, we are in front of a time series. Classical decomposition is a technique that attempts to find the main trends within time series.
Noise is everywhere. Whether you’re sampling accelerometer data for a mobile game or trying to measure the temperature of a room, noise will be there. Even if you could remove all the noise from an input device, you’ll still have a certain degree of uncertainty. If a player has tapped on the screen, where did they really wanted to tap? All these scenarios forces to re-think about how we gather and preprocess data.
