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Time series analysis involves the study of time-ordered data points and the identification of patterns, trends, and relationships within the data. Time series data is collected or recorded over a sequence of time intervals, and it is commonly used in various fields such as finance, economics, signal processing, weather forecasting, and more. Here’s an overview of key concepts and techniques in time series analysis:
I. Components of Time Series:
1. Trend:
- The long-term movement or direction in the data. Trends can be upward, downward, or stationary.
2. Seasonality:
- Repeating patterns or cycles at regular intervals, often corresponding to certain seasons, months, days of the week, etc.
3. Cyclic Patterns:
- Longer-term patterns that are not necessarily tied to specific calendar intervals.
4. Irregular or Residual:
- Random fluctuations or noise that cannot be explained by the trend, seasonality, or cyclic patterns.
II. Key Concepts and Techniques:
1. Time Series Plotting:
- Visualizing time series data through…
