Downstream¶

ImputeGAP includes a dedicated module for systematically evaluating the impact of data imputation on downstream tasks. Currently, forecasting is the primary supported task, with plans to expand to additional tasks in the future.

from imputegap.recovery.imputation import Imputation
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils

# initialize the time series object
ts = TimeSeries()

# load and normalize the timeseries
ts.load_series(utils.search_path("forecast-economy"))
ts.normalize()

# contaminate the time series
ts_m = ts.Contamination.aligned(ts.data, rate_series=0.8)

# define and impute the contaminated series
imputer = Imputation.MatrixCompletion.CDRec(ts_m)
imputer.impute()

# compute and print the downstream results
downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"}
imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)

All downstream models developed in ImputeGAP are available in the ts.forecasting_models module, which can be listed as follows:

from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"ImputeGAP downstream models for forecasting : {ts.forecasting_models}")