Time Series Analysis | Detecting Trend, Variance & Seasonality (Part 1) скачать в хорошем качестве

Time Series Analysis | Detecting Trend, Variance & Seasonality (Part 1)

What is stationarity in time series? Why does it matter in forecasting? And how do we detect and fix non-stationary data using real-world examples? In this video, we take a storytelling-detective approach to understand stationarity — a key concept in time series forecasting. Using fun analogies and practical transformations like log, differencing, and polynomial fitting, we explore: 🔍 Topics Covered: What is stationarity in time series? Types of non-stationarity: trend, variance, and seasonality How to detect stationarity visually Using Box-Cox transformation to stabilize variance Applying differencing to remove trend Understanding unit root and the concept behind ADF test Why seasonality is different from trend How to make a non-stationary series suitable for models like ARIMA, SARIMA, ARMA 🛠️ Techniques Demonstrated: Log Transform Polynomial Fit Differencing Visual checks for stationarity 👇 Why You Should Watch: If you're trying to apply ARIMA models, build reliable forecasts, or just want to understand why your model's predictions keep failing — this is the video for you. We break it all down in simple, visual English — no heavy math. 💬 Drop a comment if you’ve seen these "ghosts" in your data. ☕ Like this video to fuel the tired detective's coffee addiction. 📌 Share this with a friend still lost in the time series woods. 📬 Subscribe for more detective-style algorithm tutorials. Chapters: 0:00 Introduction 1:26 Characteristics of Time Series 3:25 Classic Regressions Vs Time Series 5:00 Series Smoothening 7:14 Linear Regression fit - Time Series 9:16 Polynomial Regression fit - Time Series 10:34 Variance 14:04 Rolling Variance 16:12 Time Series Transformations 20:00 Box Cox and Yeo Johnson Transformation 28:58 Trend in Time Series 31:12 Time Series Differencing 33:01 Problem with over Differencing 34:32 What is Stationarity? 35:54 Seasonality in Time Series 37:50 Closing and coming up (Part 2)... 🕵️‍♂️ AlgoStalk – Where Every Feature is a Suspect, and Every Data Point is a Clue. #TimeSeriesAnalysis #DataScience #MachineLearning #Forecasting #AlgoStalk