LQM Registry / ensemble
Adaptive Exponential-Weights Ensemble
Recency-aware exponential weighting over candidate loss histories with deterministic champion selection and effective-model-count diagnostics.
Model key
lq.ensemble.exponential-weightsVersion1.0.0
Runtimepython-process
Model contract
Inputs are explicit.
{
"type": "object",
"required": [
"candidates"
],
"properties": {
"candidates": {
"type": "object"
}
}
}Output contract
Outputs are explicit.
{
"type": "object",
"required": [
"weights",
"champion",
"discounted_losses",
"effective_model_count"
]
}Default parameters
{
"eta": 1,
"decay": 0.9699999999999999733546474089962430298328399658203125,
"weight_floor": 0
}Example input
{
"candidates": {
"ar1": [
0.8000000000000000444089209850062616169452667236328125,
0.6999999999999999555910790149937383830547332763671875,
0.59999999999999997779553950749686919152736663818359375,
0.5500000000000000444089209850062616169452667236328125,
0.5
],
"trend": [
0.6999999999999999555910790149937383830547332763671875,
0.61999999999999999555910790149937383830547332763671875,
0.57999999999999996003197111349436454474925994873046875,
0.5,
0.419999999999999984456877655247808434069156646728515625
],
"naive": [
0.90000000000000002220446049250313080847263336181640625,
0.88000000000000000444089209850062616169452667236328125,
0.84999999999999997779553950749686919152736663818359375,
0.82999999999999996003197111349436454474925994873046875,
0.8000000000000000444089209850062616169452667236328125
]
}
}