Fitting ARIMA and SARIMA Models for Time Series Forecasting

Unlocking patterns in historical data is key to predicting future trends, but choosing the right statistical model can feel overwhelming. This text-based course guides you through the foundational concepts of time series analysis, helping you confidently select, configure, and evaluate predictive models. You will learn how to analyze data patterns, diagnose model fit, and generate reliable forecasts using industry-standard Python libraries. What you'll learn: Understand the core concepts of stationarity, trends, and seasonality in time series data; Identify model parameters using Autocorrelation (ACF) and Partial Autocorrelation (PACF) plots; Build and fit ARIMA and SARIMA models using the statsmodels library; Evaluate model performance using AIC, BIC, and residual diagnostics; Apply modern automated model selection techniques using pmdarima; Generate and interpret future forecasts with confidence intervals. You will start with key terminology and foundational statistical concepts before moving on to practical step-by-step implementations. Through clear written explanations and structured code examples, you will progress from raw data preparation to complete forecasting workflows. This course is designed for beginners in data analysis and forecasting; basic familiarity with Python is helpful but no prior time series experience is required. Begin reading today to master the essentials of time series forecasting.