Auto ARIMA

Auto ARIMA, short for Automated ARIMA, is a variation of the ARIMA model that automates the selection of the order parameters (p, d, q) for the ARIMA model. It uses a stepwise algorithm to automatically determine the optimal values for these parameters based on the characteristics of the time series data.

The main advantage of Auto ARIMA is that it simplifies the process of selecting the ARIMA model order, which can be a complex and time-consuming task. Instead of manually analyzing the ACF and PACF plots and testing different combinations of orders, Auto ARIMA automates this process and provides a more efficient way of finding the best-fitting ARIMA model.

Method: POST Authorization: API Key

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

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
}