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series_id
stringclasses
96 values
timestamp
stringdate
2023-01-01 00:00:00
2023-06-29 23:00:00
hour_sin
float64
-1
1
hour_cos
float64
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1
dow_sin
float64
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dow_cos
float64
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1
is_weekend
float64
0
1
trend
float64
-1.73
1.27
workload_intensity
float64
0
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demand_forecast
float64
0
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staffing_forecast
float64
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2.58
upstream_quality_forecast
float64
0.41
1
promotion_intensity
float64
0
1.97
shock_risk
float64
0
1.09
maintenance_known
float64
0
1
unit_reliability_forecast
float64
0.51
1
queue_pressure_forecast
float64
0
24.8
network_pressure_forecast
float64
0
19.2
event_load_forecast
float64
0
6.95
service_irregularity_risk_forecast
float64
0
1
throughput_disruption_risk_forecast
float64
0
0.77
nominal_capacity
float64
56.2
86
zone_sin
float64
-1
1
zone_cos
float64
-1
1
target
float64
0.16
53
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End of preview. Expand in Data Studio

operations_forecasting_2026

Multivariate hourly forecasting for anonymized operations units.

Target

Predict the future hourly operational load index for each series_id. Higher values indicate more operational pressure in that unit.

Forecast Contract

  • Frequency: h
  • Series: 96
  • Timesteps per series: 4992
  • Target column: target
  • Training history length used by the baseline templates: 168
  • Rollout block length: 24
  • Required prediction horizon: validation: 336, test: 336
  • Primary metric: WAPE; lower is better.

Rollout block length. Submissions must still predict every row in the provided forecast_index_*.csv files.

Files

  • train.csv: public training data with targets.
  • validation_input.csv: validation covariates and history without validation labels.
  • forecast_index_validation.csv: exact validation timestamps to predict.
  • metadata.json: machine-readable benchmark metadata mirrored in this card.

Validation labels and private test data are not included in this public dataset repository.

Schema

  • train: series_id, timestamp, hour_sin, hour_cos, dow_sin, dow_cos, is_weekend, trend, workload_intensity, demand_forecast, staffing_forecast, upstream_quality_forecast, promotion_intensity, shock_risk, maintenance_known, unit_reliability_forecast, queue_pressure_forecast, network_pressure_forecast, event_load_forecast, service_irregularity_risk_forecast, throughput_disruption_risk_forecast, nominal_capacity, zone_sin, zone_cos, target
  • prediction: series_id, timestamp, prediction
  • labels: series_id, timestamp, target

Submission

Upload validation predictions through the course leaderboard Space. Prediction CSVs must use:

series_id,timestamp,prediction

The model name is entered in the leaderboard form and is not part of the CSV schema.

Course Links

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