Скачать или смотреть Understanding Trend, Seasonality, Cyclicity, and Noise in Time Series Analysis

Learn the fundamentals of time series analysis with Python in this beginner-friendly tutorial. Discover how to identify key patterns such as trend, seasonality, cycles, and noise using real-world data like climate records and sales figures. This lesson guides you step by step through data preparation, visualization, and basic analysis, even if you are new to Python.

Follow along with practical examples to explore and clean your data, create visualizations, and answer real questions using code. By the end, you will understand how to spot important time-based patterns and apply best practices for analyzing time series data in your own projects.

00:00 Introduction and Overview
00:23 Preparing Python and Suppressing Warnings
00:56 Loading and Exploring Time Series Data
01:37 Visualizing Data with Line Plots
02:28 Understanding Trends in Time Series
03:32 Calculating and Plotting Rolling Means
04:52 Exploring Seasonality and Monthly Patterns
05:24 Grouping Data by Month and Bar Charts
06:17 Identifying Cyclic Patterns
07:01 Using Larger Rolling Windows for Cycles
07:48 Understanding and Visualizing Noise
08:20 Removing Trends to Isolate Noise
09:10 Handling Errors and Missing Data
09:48 Creating and Removing Columns
10:54 Counting and Summarizing Cold Days
11:26 Looping Through Data for Insights
12:13 Using List Comprehensions for Analysis
12:48 Sorting and Filtering Data
13:18 Comparing Summer and Winter Temperatures
14:26 Mini Project: User Input and Custom Analysis
15:43 Best Practices for Time Series Analysis
16:20 Handling and Filling Missing Values
16:58 Identifying Numeric Columns
17:26 User Challenge: Warm Days by Month
18:07 Quick Review and Key Takeaways
18:44 Conclusion and Next Steps

#Python #TimeSeries #DataScience