Data Science

Time Series Forecasting Basics

Senior Data Analyst
May 19, 2026
5 min read

Key Concepts

  • **Trend**: Long-term direction.
  • **Seasonality**: Repeating patterns (weekly, yearly).
  • **Noise**: Random fluctuations.

    • Simple Moving Average

    df['sales_ma7'] = df['sales'].rolling(window=7).mean()

    Libraries

  • **Statsmodels**: ARIMA, ETS.
  • **Prophet**: Facebook's forecasting tool.
  • **Scikit-learn**: Treat it as regression with time features.

    • *Day 140: Building a Dashboard with Streamlit.*

    Ready to put your knowledge into practice?

    Join SQL Mastery and learn through interactive exercises.