Time-Series Analysis: Trend, Seasonality, Cyclicality, and Noise Explained Part 3
Автор: Himat Academy
Загружено: 2026-01-18
Просмотров: 58
Описание:
In this lecture, we explain the core temporal components of time-series data used in data analytics and machine learning.
You will learn how to identify and understand trend, seasonality, cyclicality, and noise, with clear explanations and visual examples.
The video covers:
What temporal components are in time-series data
Trend and its importance in forecasting
Seasonality and recurring patterns
Cyclicality vs seasonality (key differences)
Noise and random variation
Real-world examples from business, economics, and analytics
This lecture is designed for students, researchers, and professionals studying data analytics, machine learning, and AI.
📌 Suitable for:
Data Analytics courses
Time-Series Analysis
Machine Learning fundamentals
Business and economic data analysis
🔖 YouTube Tags (space separated)
time series analysis data analytics trend seasonality cyclicality noise temporal components machine learning forecasting data science statistics business analytics time series decomposition AI lecture university course data visualization
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