Time-Series Analysis: Trend, Seasonality, Cyclicality, and Noise Explained Part 3 скачать в хорошем качестве

Time-Series Analysis: Trend, Seasonality, Cyclicality, and Noise Explained Part 3

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