/plushcap/analysis/influxdata/influxdata-time-series-decomposition

What Is Time Series Decomposition? A Detailed Introduction

What's this blog post about?

Time series decomposition is a statistical process used by businesses to break down time series datasets into individual components such as trend, seasonality, and noise. This technique helps analysts discover patterns and variations within the data, making it easier to model and forecast future data points, identify anomalies, and make accurate data-driven decisions. Time series decomposition is applicable in various fields like economics, retail, healthcare, manufacturing, and logistics. Two common methods for decomposition are the additive model and the multiplicative model. Python, R, Julia, and Rust are some programming languages used for time series decomposition.

Company
InfluxData

Date published
Aug. 11, 2023

Author(s)
Charles Mahler

Word count
1269

Language
English

Hacker News points
None found.


By Matt Makai. 2021-2024.