{
  "id": 189613,
  "title": "Time series resources available for beginners.",
  "url": "/competitions/riiid-test-answer-prediction/discussion/189613",
  "author_name": "",
  "post_date": "2020-10-08T05:14:01.617412Z",
  "votes": 8,
  "comment_count": 4,
  "views": 0,
  "content": "<p>There are a lot of articles with the code-along tutorials. Here are a few:</p>\n<p><a href=\"https://www.udemy.com/share/101WWMBUoScFtXQHw=/\" target=\"_blank\">https://www.udemy.com/share/101WWMBUoScFtXQHw=/</a></p>\n<p><a href=\"https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea\" target=\"_blank\">https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea</a></p>\n<p><a href=\"https://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439\" target=\"_blank\">https://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439</a></p>\n<p><a href=\"https://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60\" target=\"_blank\">https://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60</a></p>\n<p><a href=\"https://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8\" target=\"_blank\">https://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8</a></p>\n<p><a href=\"https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b\" target=\"_blank\">https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b</a></p>\n<p><a href=\"https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7\" target=\"_blank\">https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7</a></p>\n<p><a href=\"https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23\" target=\"_blank\">https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23</a></p>\n<p><a href=\"https://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b\" target=\"_blank\">https://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b</a></p>\n<p><a href=\"https://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8\" target=\"_blank\">https://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8</a></p>\n<p><a href=\"https://www.sciencedirect.com/science/article/pii/S0169207019301876\" target=\"_blank\">https://www.sciencedirect.com/science/article/pii/S0169207019301876</a></p>\n<p>hands-on text on time-series: Forecasting: Principles and Practice<br>\nRob J Hyndman and George Athanasopoulos <a href=\"https://otexts.com/fpp3/\" target=\"_blank\">https://otexts.com/fpp3/</a></p>\n<p>Time Series Analysis in Python with statsmodels - Wes McKinney, Josey Perktold, Skipper Seabold</p>\n<p>Analytics Vidhya article on Time Series Forecasting.</p>\n<p>DigitalOcean article on Time Series Forecasting.</p>\n<p>hierarchical time series</p>\n<p>This is a hierarchical time series competition, and therefore it is important to know the literature and software available.</p>\n<p>A great resource to learn about hierarchical and grouped time series is chapter 10 of Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos - <a href=\"https://otexts.com/fpp2/hierarchical.html\" target=\"_blank\">https://otexts.com/fpp2/hierarchical.html</a> - and also the related papers and presentations:</p>\n<p>Optimal combination forecasts for hierarchical time series: <a href=\"https://www.sciencedirect.com/science/article/pii/S0167947311000971\" target=\"_blank\">https://www.sciencedirect.com/science/article/pii/S0167947311000971</a><br>\nOptimal combination forecasts for hierarchical time series (extended): <a href=\"http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf\" target=\"_blank\">http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf</a><br>\nForecasting hierarchical and grouped time series (presentation): <a href=\"https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf\" target=\"_blank\">https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf</a><br>\nThere is also an R-package implementing the main aggregation/reconciliation approaches: <a href=\"https://cran.r-project.org/web/packages/hts/index.html\" target=\"_blank\">https://cran.r-project.org/web/packages/hts/index.html</a> and its associated paper: <a href=\"https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf\" target=\"_blank\">https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf</a>.</p>",
  "messages": [
    {
      "id": "1042156",
      "postDate": "10/08/2020 05:14:01",
      "content": "<p>There are a lot of articles with the code-along tutorials. Here are a few:</p>\n<p><a href=\"https://www.udemy.com/share/101WWMBUoScFtXQHw=/\" target=\"_blank\">https://www.udemy.com/share/101WWMBUoScFtXQHw=/</a></p>\n<p><a href=\"https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea\" target=\"_blank\">https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea</a></p>\n<p><a href=\"https://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439\" target=\"_blank\">https://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439</a></p>\n<p><a href=\"https://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60\" target=\"_blank\">https://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60</a></p>\n<p><a href=\"https://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8\" target=\"_blank\">https://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8</a></p>\n<p><a href=\"https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b\" target=\"_blank\">https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b</a></p>\n<p><a href=\"https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7\" target=\"_blank\">https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7</a></p>\n<p><a href=\"https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23\" target=\"_blank\">https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23</a></p>\n<p><a href=\"https://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b\" target=\"_blank\">https://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b</a></p>\n<p><a href=\"https://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8\" target=\"_blank\">https://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8</a></p>\n<p><a href=\"https://www.sciencedirect.com/science/article/pii/S0169207019301876\" target=\"_blank\">https://www.sciencedirect.com/science/article/pii/S0169207019301876</a></p>\n<p>hands-on text on time-series: Forecasting: Principles and Practice<br>\nRob J Hyndman and George Athanasopoulos <a href=\"https://otexts.com/fpp3/\" target=\"_blank\">https://otexts.com/fpp3/</a></p>\n<p>Time Series Analysis in Python with statsmodels - Wes McKinney, Josey Perktold, Skipper Seabold</p>\n<p>Analytics Vidhya article on Time Series Forecasting.</p>\n<p>DigitalOcean article on Time Series Forecasting.</p>\n<p>hierarchical time series</p>\n<p>This is a hierarchical time series competition, and therefore it is important to know the literature and software available.</p>\n<p>A great resource to learn about hierarchical and grouped time series is chapter 10 of Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos - <a href=\"https://otexts.com/fpp2/hierarchical.html\" target=\"_blank\">https://otexts.com/fpp2/hierarchical.html</a> - and also the related papers and presentations:</p>\n<p>Optimal combination forecasts for hierarchical time series: <a href=\"https://www.sciencedirect.com/science/article/pii/S0167947311000971\" target=\"_blank\">https://www.sciencedirect.com/science/article/pii/S0167947311000971</a><br>\nOptimal combination forecasts for hierarchical time series (extended): <a href=\"http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf\" target=\"_blank\">http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf</a><br>\nForecasting hierarchical and grouped time series (presentation): <a href=\"https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf\" target=\"_blank\">https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf</a><br>\nThere is also an R-package implementing the main aggregation/reconciliation approaches: <a href=\"https://cran.r-project.org/web/packages/hts/index.html\" target=\"_blank\">https://cran.r-project.org/web/packages/hts/index.html</a> and its associated paper: <a href=\"https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf\" target=\"_blank\">https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf</a>.</p>",
      "rawMarkdown": "There are a lot of articles with the code-along tutorials. Here are a few:\n\nhttps://www.udemy.com/share/101WWMBUoScFtXQHw=/\n\nhttps://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea\n\nhttps://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439\n\nhttps://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60\n\nhttps://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8\n\nhttps://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b\n\nhttps://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7\n\nhttps://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23\n\nhttps://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b\n\nhttps://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8\n\nhttps://www.sciencedirect.com/science/article/pii/S0169207019301876\n\nhands-on text on time-series: Forecasting: Principles and Practice\nRob J Hyndman and George Athanasopoulos https://otexts.com/fpp3/\n\nTime Series Analysis in Python with statsmodels - Wes McKinney, Josey Perktold, Skipper Seabold\n\nAnalytics Vidhya article on Time Series Forecasting.\n\nDigitalOcean article on Time Series Forecasting.\n\nhierarchical time series\n\nThis is a hierarchical time series competition, and therefore it is important to know the literature and software available.\n\nA great resource to learn about hierarchical and grouped time series is chapter 10 of Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos - https://otexts.com/fpp2/hierarchical.html - and also the related papers and presentations:\n\nOptimal combination forecasts for hierarchical time series: https://www.sciencedirect.com/science/article/pii/S0167947311000971\nOptimal combination forecasts for hierarchical time series (extended): http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf\nForecasting hierarchical and grouped time series (presentation): https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf\nThere is also an R-package implementing the main aggregation/reconciliation approaches: https://cran.r-project.org/web/packages/hts/index.html and its associated paper: https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf.",
      "votes": null
    },
    {
      "id": "1042159",
      "postDate": "10/08/2020 05:20:39",
      "content": "<p>thank you for sharing </p>",
      "rawMarkdown": "thank you for sharing",
      "votes": null
    },
    {
      "id": "1042198",
      "postDate": "10/08/2020 06:03:41",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/vpfahad\" target=\"_blank\">@vpfahad</a> thanks for sharing this</p>",
      "rawMarkdown": "Hi @vpfahad thanks for sharing this",
      "votes": null
    },
    {
      "id": "1042233",
      "postDate": "10/08/2020 06:17:28",
      "content": "<p>welcome bro</p>",
      "rawMarkdown": "welcome bro",
      "votes": null
    },
    {
      "id": "1042409",
      "postDate": "10/08/2020 07:59:11",
      "content": "<p>Thank you for the share! Did you find any particular out of the list effective for multivariate analysis? Asking as I haven't visited those articles yet. Thanks again.</p>",
      "rawMarkdown": "Thank you for the share! Did you find any particular out of the list effective for multivariate analysis? Asking as I haven't visited those articles yet. Thanks again.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1042159,
      "author_name": "",
      "author_url": "",
      "post_date": "10/08/2020 05:20:39",
      "content": "<p>thank you for sharing </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1042198,
      "author_name": "vaibhavmathur96",
      "author_url": "",
      "post_date": "10/08/2020 06:03:41",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/vpfahad\" target=\"_blank\">@vpfahad</a> thanks for sharing this</p>",
      "votes": null,
      "replies": [
        {
          "id": 1042233,
          "author_name": "",
          "author_url": "",
          "post_date": "10/08/2020 06:17:28",
          "content": "<p>welcome bro</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1042409,
      "author_name": "binaicrai",
      "author_url": "",
      "post_date": "10/08/2020 07:59:11",
      "content": "<p>Thank you for the share! Did you find any particular out of the list effective for multivariate analysis? Asking as I haven't visited those articles yet. Thanks again.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1042156": "There are a lot of articles with the code-along tutorials. Here are a few:\n\nhttps://www.udemy.com/share/101WWMBUoScFtXQHw=/\n\nhttps://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea\n\nhttps://medium.com/towards-artificial-intelligence/datetime-manipulations-with-python-de57aa7e3439\n\nhttps://medium.com/@ishan.s_54240/stock-data-and-analysis-529aa9aee60\n\nhttps://medium.com/datadriveninvestor/time-series-analysis-with-r-85b7c62019f8\n\nhttps://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b\n\nhttps://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7\n\nhttps://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23\n\nhttps://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b\n\nhttps://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8\n\nhttps://www.sciencedirect.com/science/article/pii/S0169207019301876\n\nhands-on text on time-series: Forecasting: Principles and Practice\nRob J Hyndman and George Athanasopoulos https://otexts.com/fpp3/\n\nTime Series Analysis in Python with statsmodels - Wes McKinney, Josey Perktold, Skipper Seabold\n\nAnalytics Vidhya article on Time Series Forecasting.\n\nDigitalOcean article on Time Series Forecasting.\n\nhierarchical time series\n\nThis is a hierarchical time series competition, and therefore it is important to know the literature and software available.\n\nA great resource to learn about hierarchical and grouped time series is chapter 10 of Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos - https://otexts.com/fpp2/hierarchical.html - and also the related papers and presentations:\n\nOptimal combination forecasts for hierarchical time series: https://www.sciencedirect.com/science/article/pii/S0167947311000971\nOptimal combination forecasts for hierarchical time series (extended): http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp9-07.pdf\nForecasting hierarchical and grouped time series (presentation): https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2014/07/Athanasopoulos_George_ISF2014.pdf\nThere is also an R-package implementing the main aggregation/reconciliation approaches: https://cran.r-project.org/web/packages/hts/index.html and its associated paper: https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf.",
    "1042159": "thank you for sharing",
    "1042198": "Hi @vpfahad thanks for sharing this",
    "1042233": "welcome bro",
    "1042409": "Thank you for the share! Did you find any particular out of the list effective for multivariate analysis? Asking as I haven't visited those articles yet. Thanks again."
  },
  "source": "meta"
}