{
  "id": 286759,
  "title": "Vintage methods",
  "url": "/competitions/g-research-crypto-forecasting/discussion/286759",
  "author_name": "",
  "post_date": "2021-11-10T14:17:20.770664200Z",
  "votes": 13,
  "comment_count": 4,
  "views": 0,
  "content": "<p>I am currently still in exploration phase of the competition (like most of you, probably ;-) and one of the things I am checking out are classic time series methods per asset. For reference, here are two notebooks I think might come in handy:</p>\n<ul>\n<li>exponential smoothing <a href=\"https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods\" target=\"_blank\">https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods</a></li>\n<li>Prophet <a href=\"https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet\" target=\"_blank\">https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet</a></li>\n</ul>",
  "messages": [
    {
      "id": "1577954",
      "postDate": "11/10/2021 14:17:20",
      "content": "<p>I am currently still in exploration phase of the competition (like most of you, probably ;-) and one of the things I am checking out are classic time series methods per asset. For reference, here are two notebooks I think might come in handy:</p>\n<ul>\n<li>exponential smoothing <a href=\"https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods\" target=\"_blank\">https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods</a></li>\n<li>Prophet <a href=\"https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet\" target=\"_blank\">https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet</a></li>\n</ul>",
      "rawMarkdown": "I am currently still in exploration phase of the competition (like most of you, probably ;-) and one of the things I am checking out are classic time series methods per asset. For reference, here are two notebooks I think might come in handy:\n\n- exponential smoothing https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods\n- Prophet https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet",
      "votes": null
    },
    {
      "id": "1577975",
      "postDate": "11/10/2021 14:35:06",
      "content": "<p>👍  Can highly recommend watching <a href=\"https://www.kaggle.com/konradb\" target=\"_blank\">@konradb</a> 's full talk with <a href=\"https://www.kaggle.com/abhishek\" target=\"_blank\">@abhishek</a> on time-series!<br>\n<a href=\"https://www.youtube.com/watch?v=cKzXOOtOXYY\" target=\"_blank\">https://www.youtube.com/watch?v=cKzXOOtOXYY</a></p>",
      "rawMarkdown": "👍  Can highly recommend watching @konradb 's full talk with @abhishek on time-series!\nhttps://www.youtube.com/watch?v=cKzXOOtOXYY",
      "votes": null
    },
    {
      "id": "1577993",
      "postDate": "11/10/2021 14:48:10",
      "content": "<p>Thanks :-) </p>",
      "rawMarkdown": "Thanks :-)",
      "votes": null
    },
    {
      "id": "1578364",
      "postDate": "11/10/2021 23:57:50",
      "content": "<p>Just imagine a top 10 solution with <code>fbprophet</code>.</p>",
      "rawMarkdown": "Just imagine a top 10 solution with `fbprophet`.",
      "votes": null
    },
    {
      "id": "1627423",
      "postDate": "12/23/2021 21:55:27",
      "content": "<p>Prophet is a type of Holter Winters method, so just moving average :)</p>",
      "rawMarkdown": "Prophet is a type of Holter Winters method, so just moving average :)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1577975,
      "author_name": "carlolepelaars",
      "author_url": "",
      "post_date": "11/10/2021 14:35:06",
      "content": "<p>👍  Can highly recommend watching <a href=\"https://www.kaggle.com/konradb\" target=\"_blank\">@konradb</a> 's full talk with <a href=\"https://www.kaggle.com/abhishek\" target=\"_blank\">@abhishek</a> on time-series!<br>\n<a href=\"https://www.youtube.com/watch?v=cKzXOOtOXYY\" target=\"_blank\">https://www.youtube.com/watch?v=cKzXOOtOXYY</a></p>",
      "votes": null,
      "replies": [
        {
          "id": 1577993,
          "author_name": "konradb",
          "author_url": "",
          "post_date": "11/10/2021 14:48:10",
          "content": "<p>Thanks :-) </p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1578364,
      "author_name": "abdelghanibelgaid",
      "author_url": "",
      "post_date": "11/10/2021 23:57:50",
      "content": "<p>Just imagine a top 10 solution with <code>fbprophet</code>.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1627423,
      "author_name": "vladimircape",
      "author_url": "",
      "post_date": "12/23/2021 21:55:27",
      "content": "<p>Prophet is a type of Holter Winters method, so just moving average :)</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1577954": "I am currently still in exploration phase of the competition (like most of you, probably ;-) and one of the things I am checking out are classic time series methods per asset. For reference, here are two notebooks I think might come in handy:\n\n- exponential smoothing https://www.kaggle.com/konradb/practical-time-series-pt-2-smoothing-methods\n- Prophet https://www.kaggle.com/konradb/practical-time-series-pt-4-prophet",
    "1577975": "👍  Can highly recommend watching @konradb 's full talk with @abhishek on time-series!\nhttps://www.youtube.com/watch?v=cKzXOOtOXYY",
    "1577993": "Thanks :-)",
    "1578364": "Just imagine a top 10 solution with `fbprophet`.",
    "1627423": "Prophet is a type of Holter Winters method, so just moving average :)"
  },
  "source": "meta"
}