{
  "fetched_at": "2026-10-20T10:53:22Z",
  "source_url": "https://www.kaggle.com/competitions/SARIMA-Minitab-Python/data",
  "pages": [
    {
      "name": "data-description",
      "content": "In this competition, you will forecast values for six times series with different behaviours. The training data includes an identifier for each series, an index and the value of the series.\n\n## File descriptions\n\n*  **train.csv.** The training set contains four columns: the id (identifier), Serie (id from 1 to 6), indice (index), and y (the value).\n*   **test.csv.** The test set has the same features that the train set, but you will have to forecast the target values y.\n*   **sample_submission.csv.** Shows the correct format for the submission.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "## Goal of the Competition\n\nThis contest is an activity of an analytic course in Tecnológico de Monterrey for industrial engineers. The purpose is to forecast six time series (no particular topic) using Minitab and Python. Minitab is a powerful tool to monitor and optimize\nindustrial processes, how good could be forecasting stationary and non-stationary time series? This contest will be test in that direction.\n\n## Context\n\nSince this is a course activity, the objective is to test the students knowledge in the Box-Jenkins methodology the purpose is to forecast the time series by using the SARIMA model. Furthermore, since the students have an industrial engineering major the forecast will be obtained by using the statistical software Minitab. Newcomers are invited to use their prefered tools.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is **Root Mean Squared Logarithmic Error**. \n---\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `id` and `y`. \n\nThe file should contain a header and have the following format:\n```\nid,y\n791,0.0\n792,0.0\n793,0.0\n794,0.0\n795,0.0\n796,0.0\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "## One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts\n\n## No private sharing among participants\nPrivately sharing code or data with other participants is not permitted.\n\n## Individual contest\nThis is an individual contest. No teams are allowed.\n\n## Submission limits\nYou may submit a maximum of 20 entries per day.\n\n## Competition Timeline\nStart date: August 19, 2022\nEnd date: August 26, 2022\n\nCOMPETIRION SPONSOR: Kaggle",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}