SARIMA for Time Series

SARIMA – Seasonal Autoregressive Integrated Moving Average: SARIMA extends ARIMA by incorporating a seasonal pattern that repeats at regular intervals. If there are recurring patterns in the data. It considers how the past seasonal patterns contribute to forecasting future values.

e.g., sales increasing every holiday season SARIMA captures this seasonality.

  1. Autoregressive (AR): Looks at how a value relates to its past value. If today’s temperature is high, it’s likely tomorrow’s will be high too.
  2. Integrated (I): Deals with trends by differencing—subtracting each value from the previous one. If temperatures are generally rising over time, SARIMA helps look at how much they’re going up.
  3. Moving Average (MA):Considers the error from previous predictions to improve future predictions. If yesterday’s prediction was a bit off, adjust today’s prediction to be closer to the actual value.
  4. Seasonal (S): Addresses repeating patterns, like daily or yearly seasons. Acknowledges that it’s colder in winter and hotter in summer, adjusting predictions accordingly.

Understanding SARIMA in Simple Steps

  1. Exploratory Data Analysis (EDA): It understands the characteristics of the time series data. Plot the data to identify trends, seasonality, and any patterns.
  2. Stationarity Check: Ensure the data has constant statistical properties over time. It  Applies differencing to make the data stationary. This involves subtracting each value from its previous value.
  3. Autocorrelation Analysis: Understand the correlation between the current value and past values. Plot Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to identify the optimal values for AR and MA components.
  4. Model Fitting: We can use the identified parameters to fit the ARIMA or SARIMA model and can train the model using historical data, considering autoregressive, moving average, and integration components.
  5. Prediction and Evaluation: Make predictions using the fitted model and assess its performance and compare the predicted values with actual values to evaluate how well the model captures the underlying patterns in the data.

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