Understanding the SARIMA Model
What is Time Series Data?
Time series data is a sequence of data points collected or recorded at specific time intervals. Examples include daily stock prices, monthly sales data, or yearly rainfall measurements.
What is SARIMA?
SARIMA stands for Seasonal AutoRegressive Integrated Moving Average. It's a type of statistical model used for forecasting time series data that has a seasonal component.
- Seasonal: Refers to repeating patterns in the data at regular intervals, such as sales that increase every December.
- AutoRegressive (AR): Uses the relationship between an observation and a number of lagged (previous) observations.
- Integrated (I): Involves differencing the data (subtracting the previous observation from the current observation) to make it stationary (i.e., its statistical properties do not change over time).
- Moving Average (MA): Uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.
In simple terms, SARIMA helps predict future values by understanding patterns in past data, especially when there's seasonality involved.
Key Steps in the Notebook
- Data Loading and Preparation:
- The first steps usually involve loading the data and preparing it for analysis. This might include cleaning the data, handling missing values, and setting up the time series structure.
- Model Building:
- The notebook likely contains code to set up and train the SARIMA model using the historical data. This involves specifying the parameters that control the AR, I, and MA components, as well as the seasonal aspect.
- Forecasting:
- Once the model is trained, it can be used to predict future data points. The notebook includes a section where the model makes these predictions for a specified period.
- Visualization:
- The results of the forecasting are often visualized using plots. These plots show the actual data versus the predicted data, making it easier to see how well the model performs.
Interpreting the Results
- Plots: The notebook generates a plot that shows both the historical data (actual values) and the forecasted values. The forecasted values give an idea of what the model predicts for future time points.
- Forecast Values: The model provides specific numerical predictions for future time periods. These can be useful for planning and decision-making.
