SMA courss
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Catégorie :Category: nCreator TI-Nspire
Auteur Author: NSRBIKE
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 1.77 Ko KB
Mis en ligne Uploaded: 24/10/2024 - 08:32:13
Uploadeur Uploader: NSRBIKE (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4272641
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 1.77 Ko KB
Mis en ligne Uploaded: 24/10/2024 - 08:32:13
Uploadeur Uploader: NSRBIKE (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4272641
Description
Fichier Nspire généré sur TI-Planet.org.
Compatible OS 3.0 et ultérieurs.
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MOVING AVERAGES The purpose of a moving average is to smooth out variations in data: They help reduce the impact of random fluctuations or noise in data. They also reduce the impact of isolated extreme values. Moving averages create a more smoothed and consistent representation of the data, and help identify trends. There are many types of moving averages: Simple Moving Average (SMA) that we will see in this session Weighted Moving Average (WMA) Exponential Moving Average (EMA) And many more. We will focus on SMA , which already contains the main ideas. HOW DOES MOVING AVERAGE WORK? Let X ( T ) X(T) X ( T ) be a time series. The main idea is that we calculate the average of a set of consecutive data points, over a fixed pre-specified window or period. For instance, we calculate a 5-period moving average by considering the current data point and the previous 4 data points. We then compute the average of these data points. Doing this for each data point results in a new time series, called the Simple Moving Average . EXAMPLE OF A MOVING AVERAGE T X MA(5) 1 20 2 23 3 21 4 24 5 22 22 6 24 22.8 7 26 23.4 8 25 24.2 In this example, the 5-period moving average starts at period 5, and is computed as the average of the current and previous 4 data points. Made with nCreator - tiplanet.org
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Compatible OS 3.0 et ultérieurs.
<<
MOVING AVERAGES The purpose of a moving average is to smooth out variations in data: They help reduce the impact of random fluctuations or noise in data. They also reduce the impact of isolated extreme values. Moving averages create a more smoothed and consistent representation of the data, and help identify trends. There are many types of moving averages: Simple Moving Average (SMA) that we will see in this session Weighted Moving Average (WMA) Exponential Moving Average (EMA) And many more. We will focus on SMA , which already contains the main ideas. HOW DOES MOVING AVERAGE WORK? Let X ( T ) X(T) X ( T ) be a time series. The main idea is that we calculate the average of a set of consecutive data points, over a fixed pre-specified window or period. For instance, we calculate a 5-period moving average by considering the current data point and the previous 4 data points. We then compute the average of these data points. Doing this for each data point results in a new time series, called the Simple Moving Average . EXAMPLE OF A MOVING AVERAGE T X MA(5) 1 20 2 23 3 21 4 24 5 22 22 6 24 22.8 7 26 23.4 8 25 24.2 In this example, the 5-period moving average starts at period 5, and is computed as the average of the current and previous 4 data points. Made with nCreator - tiplanet.org
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