manifest.json

{
  "title": "Triple ES (Holt-Winter's)",
  "description": "Implementation of http://www.diva-portal.org/smash/get/diva2:1198551/FULLTEXT02.pdf",
  "inputDimensionality": "univariate",
  "version": "0.3.0",
  "authors": "Adam Aboode",
  "language": "Python",
  "type": "Detector",
  "learningType": "Unsupervised",
  "mainFile": "algorithm.py",
  "executionStep": {
    "parameters": [
      {
        "name": "train_window_size",
        "type": "int",
        "defaultValue": 200,
        "optional": "false",
        "description": "size of each TripleES model to predict the next timestep"
      },
      {
        "name": "period",
        "type": "int",
        "defaultValue": 100,
        "optional": "false",
        "description": "number of time units at which events happen regularly/periodically"
      },
      {
        "name": "trend",
        "type": "enum[add, mul]",
        "defaultValue": "add",
        "optional": "true",
        "description": "type of trend component"
      },
      {
        "name": "seasonal",
        "type": "enum[add, mul]",
        "defaultValue": "add",
        "optional": "true",
        "description": "type of seasonal component"
      },
      {
        "name": "random_state",
        "type": "int",
        "defaultValue": 42,
        "optional": "true",
        "description": "Seed for random number generation."
      }
    ],
    "modelInput": "none"
  }
}