What Are the Main Steps in Time Series Regression Analysis?
The main steps in conducting a time series regression analysis include:
Data Collection: Gathering time series data from reliable sources. Data Cleaning: Handling missing values, outliers, and other anomalies. Exploratory Data Analysis: Visualizing the data to identify patterns, trends, and seasonality. Model Selection: Choosing an appropriate time series regression model (e.g., ARIMA, SARIMA). Model Fitting: Estimating the parameters of the selected model using historical data. Model Validation: Assessing the model's performance using techniques like cross-validation. Forecasting: Using the model to make predictions about future health events.
