Zero-shot forecasting using the Mimosa foundation model.
Name | Description | Type |
---|---|---|
dataset | A Sulie Dataset or pd.DataFrame with your time series data. | required |
horizon | Number of time steps to forecast into the future. | required |
target_col | Column name for the variable to forecast. | optional |
id_col | Column name to group data by (e.g., different locations). | optional |
timestamp_col | Column containing the timestamp. | optional |
quantiles | Prediction quantiles, e.g. [0.2, 0.8] | optional |
aggr | Aggregation function to use when grouping (sum or mean ). | optional |
frequency | Time series frequency (e.g., H for hourly, D for daily). | optional |
model | Specifies the model to use (defaults to the latest Mimosa version). | optional |
Forecast
object.
The Forecast
object includes two variables: median
and quantiles
, corresponding to different certainty levels in the predictions. These help you understand the range of possible outcomes, from conservative to optimistic.
You can also visualize the forecasts directly by calling the plot function:
evaluate
function measures the performance of a time series forecasting model
by sampling random time windows from the input dataset, generating forecasts, and
calculating forecast errors. Designed for robustness, it supports evaluation across
single or multiple time series and computes metrics like Weighted Quantile Loss (WQL)
or Weighted Absolute Percentage Error (WAPE). By running multiple iterations, it ensures
reliable aggregation of results, providing insights into the model’s accuracy and
adaptability to various time windows. This function is ideal for validating forecasting
models in scenarios with seasonality, trends, or diverse time series data.
Name | Description | Type |
---|---|---|
dataset | A Sulie Dataset or pd.DataFrame with your time series data. | required |
horizon | Number of time steps to forecast into the future. | required |
target_col | Column name for the variable to forecast. | optional |
id_col | Column name to group data by (e.g., different locations). | optional |
metric | Name of the validation metric, MAE , WQL , WAPE . | optional |
metric_aggregation | How a final score is calculated, mean , median . | optional |
iterations | Number of iterations to run. | optional |
model | Specifies the model to use (defaults to the latest Mimosa version). | optional |