Theta

The Theta model is a forecasting technique that combines a simple exponential smoothing approach with a linear trend component. It is useful for forecasting time series data that exhibits a linear trend.

The Theta model is based on the assumption that the future values of a time series can be estimated by applying exponential smoothing to the linearly detrended data. It is named after the Greek letter theta (θ), which represents the slope of the trend line.

The Theta model provides a simple and straightforward approach to forecasting time series data with a linear trend. However, it assumes that the trend remains linear over the forecasted period and may not capture more complex trend patterns. Additionally, it may not perform well for time series with irregular or nonlinear trends.

Method: POST Authorization: API Key

https://engine.raccoon-ai.io/api/v1/ml/time-series/theta

Authorization

Type	Key	Value
API Key	X-Api-Key	rae_######

Request Body

Section	Key	Data Type	Required	Description
train	data	json	true	Data that use to train the model
	date_col	string	true	Input features (X)
	target_col	string	true	Output targets (y)
config	freq	string	false	Gap between datas/ time
	test_size	float	false	Test size for split data
forcast	forcast_for	int	true	Number of points that need to forcast

Types

{
  "train": {
    "data": <json>,
    "dates_col": <string>,
    "target_col": <string>
  },
  "config": {
    "freq": <string>,
    "test_size": <float>
  },
  "forcast_for": <int>
}

Sample

{
  "train": {
    "data": {
      "dates": {
        "0": "2022-11-25",
        "1": "2022-12-02",
        "2": "2022-12-09",
        "3": "2022-12-16",
        "4": "2022-12-23",
        "5": "2022-12-30",
        "6": "2023-01-06",
        "7": "2023-01-13",
        "8": "2023-01-20",
        "9": "2023-01-27"
      },
      "marks": {
        "0": 161,
        "1": 123,
        "2": 134,
        "3": 167,
        "4": 143,
        "5": 156,
        "6": 167,
        "7": 143,
        "8": 156,
        "9": 167
      }
    },
    "dates_col": "dates",
    "target_col": "marks"
  },
  "config": {
    "freq": "W",
    "test_size": 0.25
  },
  "forcast_for": 5
}

Reponse Body

Key	Data Type	Description
success	boolean	Indicate the success of the request
msg	string	Message indicators
error	string	Error information, only set if success is false
result	json	Result, only set if success is true
score	json	r2_scores of the training and testing phases, only set if success is true
generated_ts	float	Generated timestamp

Types

{
  "success": <boolean>,
  "msg": <string | null>,
  "error": <string | null>,
  "result": <list>,
  "score": {
    "rmse": <float>
  },
  "generated_ts": <timestamp>
}

Sample

{
  "success": true,
  "msg": "Model trained and predicted successfully",
  "error": null,
  "result": [
    145.306460384454, 159.01596373700463, 165.26655276778865,
    144.69950483108218, 160.2060306291415
  ],
  "score": {
    "rmse": 14.027130973766175
  },
  "generated_ts": 1685514898.064395
}