Practical Time Series Analysis by Pal Dr. Avishek

Author:Pal, Dr. Avishek. [Неизв.]
Language: eng
Format: epub
Tags: COM018000 - COMPUTERS / Data Processing, COM062000 - COMPUTERS / Data Modeling and Design, COM051360 - COMPUTERS / Programming Languages / Python
Publisher: Packt Publishing
Published: 2017-10-05T09:04:46+00:00

Solving the previous equation will lead to the following variance:

Here, T is the length of the time series. For unit variance of series xT, the variance captured by the forecasted series will vary based on the smoothing parameter α as follows:

Figure 3.10: Variance captured by simple exponential smoothing with varying alpha

Second order exponential smoothing

If first order exponential smoothing does not perform well, then there is a trend in the time series data. The trend is commonly observed in many domains such as when marketing campaigns are run by e-commerce companies, the sales rise or any good annual performance by a company will have a bullish effect on its stock prices. The linear trend can occur due to linear trend between time and response:

xt = constant + ωt + εt

Here, ω is the coefficient that leads to trend. The second order exponential smoothing helps capture the trend in time series data by including another term to the first order exponential smoothing as follows:

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.