Time series analysis is a statistical technique used to analyze data points collected over time. This tutorial will guide you through the fundamentals and practical applications of time series analysis using Python.

Key Concepts 🔍

  • Trend Identification: Detecting long-term movements in data
    trend_analysis
  • Seasonality: Recognizing periodic patterns (e.g., daily, monthly)
    seasonality_patterns
  • Stationarity: Ensuring data has consistent statistical properties over time
    stationarity_check

Tools & Libraries 🛠️

  • Pandas: For data manipulation and analysis
  • NumPy: Mathematical operations on time series data
  • Matplotlib/Seaborn: Visualization of time series trends
    data_visualization
  • Statsmodels: Statistical models for time series forecasting

Hands-On Example 📊

  1. Load data using pandas.read_csv()
  2. Plot time series with matplotlib.pyplot.plot()
  3. Apply moving averages to smooth trends
  4. Use ARIMA models for forecasting
    arima_forecasting

Expand Your Knowledge 📚

For deeper insights into machine learning applications in time series analysis, check out our Advanced Time Series with ML tutorial.