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Analyzing Time Series Data

Analyzing Time Series Data Please sign up by clicking on the link below (newsletter) for latest insights: https://valuechord.com/valuepulse/ 0:00 Introduction 1:10 What is Time Series Data? 1:53 Time Series Forecasting is Everywhere 2:33 Netflix example 3:05 The Four Components of Time Series 3:45 Visualizing the Components 4:55 Data Partitioning 5:34 The Humbling Naive Forecast 5:56 Regression-Based Forecasting 6:22 Capturing Trend with Regression 7:44 Capturing Seasonality 8:22 The Complete Model (Trend + Seasonality) 9:42 Autocorrelation 10:17 Smoothing Methods 10:49 Moving Average 11:32 Exponential Smoothing 12:21 Types of Exponential Smoothing 12:58 Hold-Winters Exponential Smoothing 13:39 Regression versus Smoothing: When to Use What? 14:20 Evaluating Your Forecast 15:06 Is Your Series Even Predictable? (Random Walk) 15:56 Combining Methods 16:34 Common Pitfalls to Avoid 17:18 Conclusion ------------------------------------------------------------------------------------------------------------------------------ My gear for shooting this video: 💻 Macbook Pro Laptop: https://amzn.to/4kx9n73 🖥️ Acer Monitor: https://amzn.to/4dpZrd0 My Book: Nair, A. 2025. Structured Thinking for Smarter Decisions. How 100+ Proven Frameworks Help You See Patterns, Gain Clarity, and Act with Confidence. https://www.amazon.com/dp/B0FNCXPT1C Recommended books for learning more about Multiple Linear Regression, Data Mining & Deep Learning: Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.". https://amzn.to/3ZxF2wX Goodfellow, I., Bengio, Y. & Courville, A. (2017). Deep Learning (Adaptive Computation and Machine Learning Series). Cambridge, MA, USA: MIT press. https://amzn.to/4k49AP9 Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan Kaufmann. https://amzn.to/4jjoy2P Kazil, J., & Jarmul, K. (2016). Data wrangling with python: tips and tools to make your life easier. "O'Reilly Media, Inc.". https://amzn.to/3SNp5Pr Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in Python. John Wiley & Sons. https://amzn.to/3SP8YAW Let’s connect online: 📩 ValuePulse - The weekly digest: https://valuechord.com/valuepulse/ 🐦 Twitter 1: /dr_nair (Personal) 🐦 Twitter 2: /valuechord (Company) 👨🏻‍💻 LinkedIn 1: /dranandnair (Personal) 👨🏻‍💻 LinkedIn 2: /valuechord (Company) 🖋 Blog: https://www.anandnair.com/