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🌟 Master Time Series Analysis and Forecasting in Python! 🌟
This crash course is your ultimate guide to mastering time series analysis and forecasting using Python. Whether you're new to time series or want to sharpen your skills, this course has everything you need to succeed. From essential concepts to advanced techniques, you’ll learn how to handle time series data, build models, and forecast like a pro.
The course covers key topics, including simple, double, and triple exponential smoothing (Holt-Winters method), model evaluation metrics such as MAE, RMSE, and MAPE, and advanced forecasting models like ARIMA, SARIMA, and SARIMAX. You’ll also dive into practical implementations like daily data preprocessing, cross-validation for time series, and parameter tuning to ensure accurate predictions. With hands-on Python tutorials, you’ll follow step-by-step implementations that make complex concepts easy to understand.
By the end of this course, you’ll be able to preprocess time series data, build accurate models, evaluate your results, and confidently predict the future. Ideal for data scientists, machine learning enthusiasts, business analysts, or anyone looking to make data-driven decisions through time series forecasting.
Keywords: Time Series Analysis, Python Time Series, Forecasting Techniques, Exponential Smoothing, ARIMA Models, Cross-Validation for Time Series, Model Evaluation Metrics, Predicting the Future.
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Chapters
00:00 Intro: Time Series Analysis
1:50 Understanding Time Series Data
4:16 Python Setup: Libraries & Data
11:03 Mastering Time Series Indexing
19:10 Data Exploration: Key Metrics
28:53 Time Series Data Visualization
36:59 Data Manipulation for Forecasting
41:56 Time Series: Seasonal Decomposition
51:12 Visualizing Seasonal Patterns
1:00:03 Analyzing Seasonal Components
1:13:14 Autocorrelation in Time Series
1:20:11 Partial Autocorrelation (PACF)
1:27:52 Building a Useful Code Script
1:31:53 Stock Price Prediction
1:36:51 Learning from Forecast Flops
1:41:56 Introduction to Exponential Smoothing
1:44:30 Case Study: Customer Complaints
1:47:58 Simple Exponential Smoothing
2:26:56 Double Exponential Smoothing
2:41:29 Triple Exponential Smoothing (Holt-Winters)
2:45:44 Model Evaluation: Error Metrics
3:03:12 Forecasting the Future
3:09:41 Holt-Winters with Daily Data
3:21:11 Holt-Winters: Pros and Cons
3:26:08 Capstone Project Introduction
3:30:00 Capstone Project Implementation
3:53:03 Introduction to ARIMA Models
4:16:03 Understanding Auto-Regressive (AR)
4:20:43 Stationarity and Integration (I)
4:25:56 Augmented Dickey-Fuller Test
4:33:54 Moving Average (MA) Component
4:36:41 Implementing the ARIMA Model
4:53:03 Introduction to SARIMA
5:06:32 Introduction to SARIMAX Models
5:16:36 Cross-Validation for Time Series
5:33:04 Parameter Tuning for Time Series
6:13:41 SARIMAX Model
6:13:47 Free eBooks, prompt engineering