Insufficient historical data for time series analysis:
Time series analysis usually requires a longer history of data to effectively capture patterns and trends. One year of data may not be sufficient for some time series models. The alternative approaches, such as simpler regression models. Additionally, you may want to explore the possibility of obtaining additional historical data if possible and appropriate.
Lack of Geometry Column for Geospatial Analysis:
Geospatial analysis requires a geometry column that specifies the spatial location of data points, such as latitude and longitude or polygon shapes. If your dataset contains county and state information, you can potentially obtain the geometry data (e.g., shapefiles or GeoJSON) for counties and states and then join this spatial information with your dataset using a common identifier like county or state codes.
Ensemble methods and small datasets:
Ensemble methods such as Random Forests can indeed be prone to overfitting in small datasets. The problem by using regularization techniques, reducing model complexity, or considering alternative modeling methods such as linear regression or simpler machine learning algorithms.