Day 38 unobserved components model (ucm)

Check out our live web application for this program - https://newdaynewlearning.netlify.app/

[!NOTE] There is a game waiting for you today, the best/first answer can win an exciting gift🎁

• Click on the link : https://www.nytimes.com/games/wordle/index.html
• You will get a 6 chance to guess the 5-letter word correctly

More about me:

I am just a Colleague of your’ s, Learning and exploring how Math, Business, and Technology can help us to make better decisions in the field of data science.

• Check out my Second brain:  https://medium.com/@ravikumar10593/
• Check out my Portfolio : Link

Topic : Unobserved Components Model (UCM) 

Article Source :

• Time Series Analysis using Unobserved Components Model in Python
• Unobserved components model (UCM)
• Time Series Modeling with Unobserved Components

TL;DR :

The Unobserved Components Model (UCM) and the ARIMA model are both used for time series analysis and forecasting, but they differ in their approach. ARIMA models focus on capturing statistical patterns in the data using autoregressive (AR), integrated (I), and moving average (MA) components, while UCMs decompose the time series into underlying, unobserved components like trend, seasonality, and cycle. 

Core Idea: UCMs are based on the idea that a time series can be broken down into distinct, unobserved components that capture different aspects of its behavior.