{ "id": 291478, "title": "[Survey] How to judge chaos or not?", "url": "/competitions/g-research-crypto-forecasting/discussion/291478", "author_name": "John Doe", "post_date": "2021-11-29T13:23:00.740000", "votes": 2, "comment_count": 0, "views": 0, "content": "

This is my memorandum.

\n

As I mentioned in the discussion, before doing EDA and apply some mathematical models to data, I will check the data has linearity or not.
\nSo I briefly surveyed how to do that(surrogate data testing). Most of them in the list are from Japanse sources. Sorry for that.
\nIf I find other any resources. I will add them to the list.
\nAlso, I will prepare the tutorial notebook for this.

\n\n
    \n

  1. Surrogate data for hypothesis testing of physical systems

    2. \n

  2. Lyapunov exponent

    3. \n

  3. Wayland method can rate some datasets as less deterministic than their random shuffle surrogates

    4. \n

  4. There is No Chaos in Stock Markets

    5. \n
\n\n
    \n

  1. サロゲート法

    2. \n

  2. 非線形時系列解析によるカオス性検定

    3. \n

  3. サロゲートデータ生成

    4. \n

  4. カオス・フラクタル 講義ノート #4

    5. \n

  5. カオス時系列の基礎とニューラルネットワーク

    6. \n

  6. リアプノフ指数

    7. \n

  7. 【時系列】時系列データ予測　予測手法マップ

    8. \n
", "messages": [ { "id": 1599484, "postDate": "2021-11-29T13:23:00.740Z", "content": "

This is my memorandum.

\n

As I mentioned in the discussion, before doing EDA and apply some mathematical models to data, I will check the data has linearity or not.
\nSo I briefly surveyed how to do that(surrogate data testing). Most of them in the list are from Japanse sources. Sorry for that.
\nIf I find other any resources. I will add them to the list.
\nAlso, I will prepare the tutorial notebook for this.

\n\n
    \n

  1. Surrogate data for hypothesis testing of physical systems

    2. \n

  2. Lyapunov exponent

    3. \n

  3. Wayland method can rate some datasets as less deterministic than their random shuffle surrogates

    4. \n

  4. There is No Chaos in Stock Markets

    5. \n
\n\n
    \n

  1. サロゲート法

    2. \n

  2. 非線形時系列解析によるカオス性検定

    3. \n

  3. サロゲートデータ生成

    4. \n

  4. カオス・フラクタル 講義ノート #4

    5. \n

  5. カオス時系列の基礎とニューラルネットワーク

    6. \n

  6. リアプノフ指数

    7. \n

  7. 【時系列】時系列データ予測　予測手法マップ

    8. \n
", "rawMarkdown": "This is my memorandum.\n\nAs I mentioned in [the discussion](https://www.kaggle.com/c/g-research-crypto-forecasting/discussion/291468), before doing EDA and apply some mathematical models to data, I will check the data has linearity or not.\nSo I briefly surveyed how to do that(surrogate data testing). Most of them in the list are from Japanse sources. Sorry for that.\nIf I find other any resources. I will add them to the list.\nAlso, I will prepare the tutorial notebook for this.\n\n- English\n1. [Surrogate data for hypothesis testing of physical systems](https://www.sciencedirect.com/science/article/pii/S0370157318301340)\n\n2. [Lyapunov exponent](https://en.wikipedia.org/wiki/Lyapunov_exponent)\n\n3. [Wayland method can rate some datasets as less deterministic than their random shuffle surrogates](https://www.keisu.t.u-tokyo.ac.jp/data/2005/METR05-05.pdf)\n\n3. [There is No Chaos in Stock Markets](https://www.researchgate.net/publication/263005024_There_is_No_Chaos_in_Stock_Markets)\n\n- Japanese\n1. [サロゲート法](http://www.simulation-studies.org/research/academicstaffs/nakamura/surrogate_data_method/#:~:text=%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E6%B3%95%E3%81%AF%E3%80%81%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E3%83%87%E3%83%BC%E3%82%BF,%E8%AA%BF%E3%81%B9%E3%82%8B%E6%96%B9%E6%B3%95%E3%81%AE%E3%81%93%E3%81%A8%E3%81%A7%E3%81%99%E3%80%82&text=%E3%81%93%E3%81%AE%E6%89%8B%E6%B3%95%E3%81%AF%E3%80%81%E8%87%AA%E5%88%86%E3%81%8C,%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E3%83%87%E3%83%BC%E3%82%BF%E3%81%A8%E8%A8%80%E3%81%84%E3%81%BE%E3%81%99%E3%80%82)\n\n2. [非線形時系列解析によるカオス性検定](https://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/pdf/1136-4.pdf)\n\n3. [サロゲートデータ生成](https://qiita.com/aya-chan/items/f1e2f746531a3393c5e6)\n\n4. [カオス・フラクタル 講義ノート #4](https://ocw.hokudai.ac.jp/wp-content/uploads/2016/01/ChaosFractal-2011-Note-04.pdf)\n\n5. [カオス時系列の基礎とニューラルネットワーク](https://logics-of-blue.com/%E3%82%AB%E3%82%AA%E3%82%B9%E6%99%82%E7%B3%BB%E5%88%97%E3%81%AE%E5%9F%BA%E7%A4%8E%E3%81%A8%E3%83%8B%E3%83%A5%E3%83%BC%E3%83%A9%E3%83%AB%E3%83%8D%E3%83%83%E3%83%88%E3%83%AF%E3%83%BC%E3%82%AF/)\n\n6. [リアプノフ指数](https://ja.wikipedia.org/wiki/%E3%83%AA%E3%82%A2%E3%83%97%E3%83%8E%E3%83%95%E6%8C%87%E6%95%B0)\n\n7. [【時系列】時系列データ予測　予測手法マップ](https://qiita.com/sato235/items/48022d2bf023ecd5ceef)\n", "votes": 1 } ], "comments": [], "raw_markdown_by_id": { "1599484": "This is my memorandum.\n\nAs I mentioned in [the discussion](https://www.kaggle.com/c/g-research-crypto-forecasting/discussion/291468), before doing EDA and apply some mathematical models to data, I will check the data has linearity or not.\nSo I briefly surveyed how to do that(surrogate data testing). Most of them in the list are from Japanse sources. Sorry for that.\nIf I find other any resources. I will add them to the list.\nAlso, I will prepare the tutorial notebook for this.\n\n- English\n1. [Surrogate data for hypothesis testing of physical systems](https://www.sciencedirect.com/science/article/pii/S0370157318301340)\n\n2. [Lyapunov exponent](https://en.wikipedia.org/wiki/Lyapunov_exponent)\n\n3. [Wayland method can rate some datasets as less deterministic than their random shuffle surrogates](https://www.keisu.t.u-tokyo.ac.jp/data/2005/METR05-05.pdf)\n\n3. [There is No Chaos in Stock Markets](https://www.researchgate.net/publication/263005024_There_is_No_Chaos_in_Stock_Markets)\n\n- Japanese\n1. [サロゲート法](http://www.simulation-studies.org/research/academicstaffs/nakamura/surrogate_data_method/#:~:text=%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E6%B3%95%E3%81%AF%E3%80%81%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E3%83%87%E3%83%BC%E3%82%BF,%E8%AA%BF%E3%81%B9%E3%82%8B%E6%96%B9%E6%B3%95%E3%81%AE%E3%81%93%E3%81%A8%E3%81%A7%E3%81%99%E3%80%82&text=%E3%81%93%E3%81%AE%E6%89%8B%E6%B3%95%E3%81%AF%E3%80%81%E8%87%AA%E5%88%86%E3%81%8C,%E3%82%B5%E3%83%AD%E3%82%B2%E3%83%BC%E3%83%88%E3%83%87%E3%83%BC%E3%82%BF%E3%81%A8%E8%A8%80%E3%81%84%E3%81%BE%E3%81%99%E3%80%82)\n\n2. [非線形時系列解析によるカオス性検定](https://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/pdf/1136-4.pdf)\n\n3. [サロゲートデータ生成](https://qiita.com/aya-chan/items/f1e2f746531a3393c5e6)\n\n4. [カオス・フラクタル 講義ノート #4](https://ocw.hokudai.ac.jp/wp-content/uploads/2016/01/ChaosFractal-2011-Note-04.pdf)\n\n5. [カオス時系列の基礎とニューラルネットワーク](https://logics-of-blue.com/%E3%82%AB%E3%82%AA%E3%82%B9%E6%99%82%E7%B3%BB%E5%88%97%E3%81%AE%E5%9F%BA%E7%A4%8E%E3%81%A8%E3%83%8B%E3%83%A5%E3%83%BC%E3%83%A9%E3%83%AB%E3%83%8D%E3%83%83%E3%83%88%E3%83%AF%E3%83%BC%E3%82%AF/)\n\n6. [リアプノフ指数](https://ja.wikipedia.org/wiki/%E3%83%AA%E3%82%A2%E3%83%97%E3%83%8E%E3%83%95%E6%8C%87%E6%95%B0)\n\n7. [【時系列】時系列データ予測　予測手法マップ](https://qiita.com/sato235/items/48022d2bf023ecd5ceef)\n" } }