Time Series: Seasonal Autoregressive Integrated Moving Average (SARIMA)
Time Series: Seasonal Autoregressive Integrated Moving Average (SARIMA) What is Time Series Analysis? Time series analysis can be used to examine historical data, identify any underlying trends, and identify seasonal fluctuations. The term "trend" describes the overarching direction the data is moving in; it can be either upward or downward. The regular variations in the data that occur on a seasonal basis are referred to as seasonal variations. This might be a weekly fluctuation, with some days often having higher or lower sales than other days, or it might be a monthly or quarterly variation. When budgeting and forecasting, trends and seasonal fluctuations can be quite helpful in making predictions about the future. There are various types of time series analysis techniques and models that are commonly used to analyze and forecast time series data. One of the prominent ones is the Seasonal Autoregressive Integrated Moving Average (SARIMA). Seasonal Autoregr...