Overview: This post introduces the basics of time series analysis and forecasting using ARIMA and SARIMA models. Learn how to prepare your data, select the right model, and interpret results for real-world forecasting tasks.
1. Understanding Time Series Data
Time series data is a sequence of data points collected or recorded at time-ordered intervals. Examples include stock prices, weather data, and sales figures.
2. Data Preparation
Key steps include handling missing values, resampling, and visualizing trends and seasonality. Proper preprocessing is crucial for accurate forecasting.
3. Model Selection
ARIMA and SARIMA are popular models for time series forecasting. I discuss how to choose parameters (p, d, q) and extend to seasonal data with SARIMA.
4. Evaluation & Interpretation
Evaluate your model using metrics like MAE and RMSE. Visualize predictions versus actuals to assess performance and make improvements.
Want the code? Check out the GitHub repo (coming soon).