Time Series

Data are often sparse in time, non-stationary, carry seasonality pattern and trends. A frequent requirement for time series techniques is that the data be stationary. This argument holds for the time series models supported here as well. This includes aggregation, resampling, interpolation to fill missing values and more. Time series data often carry seasonality pattern and trends and are non-stationary.