![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8273630%2Fe87daaf42fa06c79bd47290c6842f39c%2FDoubt.png?generation=1729402919100164&alt=media\")
Hello all,
\nWishing you the best for the competition! This is my favourite data science topic, as a Finance post-grad and I am excited to witness such a competition after a long wait! Hope the below materials help you onboard well and efficiently-
\nWishing you the best for the assignment and happy learning!
", "messages": [ { "id": "3017144", "postDate": "10/14/2024 14:52:31", "content": "Hello all,
\nWishing you the best for the competition! This is my favourite data science topic, as a Finance post-grad and I am excited to witness such a competition after a long wait! Hope the below materials help you onboard well and efficiently-
\nWishing you the best for the assignment and happy learning!
", "rawMarkdown": "Hello all,\n\nWishing you the best for the competition! This is my favourite data science topic, as a Finance post-grad and I am excited to witness such a competition after a long wait! Hope the below materials help you onboard well and efficiently-\n\n# [Past edition - Jane Street Market Prediction](https://www.kaggle.com/competitions/jane-street-market-prediction/overview)\n## Most voted kernels\n- https://www.kaggle.com/code/carlmcbrideellis/jane-street-eda-of-day-0-and-feature-importance\n- https://www.kaggle.com/code/hamditarek/market-prediction-xgboost-with-gpu-fit-in-1min\n- https://www.kaggle.com/code/aimind/bottleneck-encoder-mlp-keras-tuner-8601c5\n- https://www.kaggle.com/code/gogo827jz/jane-street-neural-network-starter\n- https://www.kaggle.com/code/muhammadmelsherbini/jane-street-extensive-eda-pca-starter\n- https://www.kaggle.com/code/tarlannazarov/own-jane-street-with-keras-nn\n- https://www.kaggle.com/code/odins0n/exploring-time-series-plots-beginners-guide\n- https://www.kaggle.com/code/jorijnsmit/found-the-holy-grail-grouptimeseriessplit\n- https://www.kaggle.com/code/snippsy/bottleneck-encoder-mlp-keras-tuner\n- https://www.kaggle.com/code/a763337092/blending-tensorflow-and-pytorch\n\n## Top ranked solutions\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348 -- rank 1\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713 -- rank 3\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837 -- rank 10\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/269181 -- rank 15\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224079 -- rank 23\n\n# Similar Kaggle competition references\n### [ASHRAE - Great Energy Predictor III](https://www.kaggle.com/competitions/ashrae-energy-prediction)\n#### Kernels- top 5 voted \n1. https://www.kaggle.com/code/caesarlupum/ashrae-start-here-a-gentle-introduction\n2. https://www.kaggle.com/code/nroman/eda-for-ashrae\n3. https://www.kaggle.com/code/rohanrao/ashrae-half-and-half\n4. https://www.kaggle.com/code/corochann/optuna-tutorial-for-hyperparameter-optimization\n5. https://www.kaggle.com/code/corochann/ashrae-training-lgbm-by-meter-type\n\n#### Kernels- top 3 scores\n1. https://www.kaggle.com/code/patrick0302/postprocessed-models-bland-by-leak\n2. https://www.kaggle.com/code/vladimirsydor/add-leak\n3. https://www.kaggle.com/code/gpamoukoff/ashrae-subm-stack-fin-3\n\n### High scoring approaches and discussions- \n1. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124709 -- rank1\n2. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/123481 -- rank2\n3. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124984 -- rank3\n4. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124788 --rank4\n5. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/127086 --rank5\n6. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/123525 -- rank9\n\n### Other useful posts and discussions\n1. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/112872\n2. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/114345\n3. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/113678\n4. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/115698\n\n## [Optiver- Trading at the Close](https://www.kaggle.com/competitions/optiver-trading-at-the-close)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ravi20076/optiver-baseline-models\n2. https://www.kaggle.com/code/yuanzhezhou/baseline-lgb-xgb-and-catboost\n3. https://www.kaggle.com/code/a27182818/explain-the-data-lightgbm-baseline\n4. https://www.kaggle.com/code/lblhandsome/optiver-robust-best-single-model\n5. https://www.kaggle.com/code/verracodeguacas/fold-cv\n6. https://www.kaggle.com/code/peizhengwang/best-public-score\n7. https://www.kaggle.com/code/siddhvr/optiver-trading-at-the-close-sub\n\n### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/487446\n2. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/486868\n3. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/462653\n4. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/486086\n\n## [Enefit - Predict Energy Behavior of Prosumers](https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/rafiko1/enefit-xgboost-starter\n2. https://www.kaggle.com/code/vitalykudelya/explain-dataset-and-baseline\n3. https://www.kaggle.com/code/vincentschuler/enefit-baseline-cross-validation\n4. https://www.kaggle.com/code/greysky/enefit-generic-notebook\n5. https://www.kaggle.com/code/vitalykudelya/enefit-target-diff\n6. https://www.kaggle.com/code/ahmedabdulwahab/pandas-data-description-and-starters-guide\n\n### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472793\n2. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499938\n3. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499397\n4. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499649\n5. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472537\n6. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472537\n\n## Playground Time Series Forecasting challenges\n### [Season3-Episode 20](https://www.kaggle.com/competitions/playground-series-s3e20)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ambrosm/pss3e20-eda-which-makes-sense\n2. https://www.kaggle.com/code/kacperrabczewski/rwanda-co2-step-by-step-guide\n3. https://www.kaggle.com/code/yaaangzhou/pg-s3-e20-eda-modeling\n4. https://www.kaggle.com/code/dmitryuarov/ps3e20-rwanda-emission-advanced-fe-20-88\n5. https://www.kaggle.com/code/iqbalsyahakbar/ps3e20-time-series-for-beginners\n\n### [Season3-Episode 19](https://www.kaggle.com/competitions/playground-series-s3e19)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/tumpanjawat/s3e19-course-eda-fe-lightgbm\n2. https://www.kaggle.com/code/iqbalsyahakbar/ps3e19-time-series-for-beginners\n3. https://www.kaggle.com/code/ivyzang/1st-place-solution-less-is-more\n4. https://www.kaggle.com/code/tetsutani/ps3e19-eda-ensemble-ml-pipeline-rnn-by-skorch\n5. https://www.kaggle.com/code/kacperrabczewski/last-minute-forecasting\n\n### [TPS- September2022](https://www.kaggle.com/competitions/tabular-playground-series-sep-2022)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting\n2. https://www.kaggle.com/code/cabaxiom/tps-sep-22-eda-and-linear-regression-baseline\n3. https://www.kaggle.com/code/khashayarrahimi94/why-you-should-not-use-correlation\n4. https://www.kaggle.com/code/vencerlanz09/tps-eda-9-models-explanation\n5. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis\n\n### [TPS- January2022](https://www.kaggle.com/competitions/tabular-playground-series-jan-2022)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model\n2. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense\n3. https://www.kaggle.com/code/usharengaraju/tensorflow-tf-data-keraspreprocessinglayers-w-b\n4. https://www.kaggle.com/code/teckmengwong/tps2201-hybrid-time-series\n5. https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex\n\n## [GoDaddy - Microbusiness Density Forecasting](https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/overview)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/titericz/better-xgb-baseline\n2. https://www.kaggle.com/code/cdeotte/linear-regression-baseline-lb-1-092\n3. https://www.kaggle.com/code/tanmay111999/gdmbf-ar-ma-arma-arima-sarima-auto-arima\n4. https://www.kaggle.com/code/cdeotte/seasonal-model-with-validation-lb-1-091\n5. https://www.kaggle.com/code/kimtaehun/complete-baseline-code-with-various-ml-model\n\n#### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/395131 -- rank1\n2. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/395264 -- rank2\n3. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/418287 -- rank3\n4. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/394821 -- rank4\n\n## [WiDS Datathon 2023](https://www.kaggle.com/competitions/widsdatathon2023)\n### Kernels- most voted \n1. https://www.kaggle.com/code/iamleonie/wids-datathon-2023-forecasting-with-lgbm\n2. https://www.kaggle.com/code/kooaslansefat/wids-2023-woman-life-freedom\n3. https://www.kaggle.com/code/khsamaha/eda-wids-datathon-2023-r\n\n## Miscellaneous discussion references on time series- \n1. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574\n2. https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/discussion/46602\n3. https://www.kaggle.com/competitions/demand-forecasting-kernels-only/discussion/63568\n4. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/133463\n5. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/135713\n6. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/134707\n\n## Miscellaneous kernel references on time series- \n1. https://www.kaggle.com/code/ravi20076/optiver-baseline-models\n2. https://www.kaggle.com/code/kanncaa1/time-series-prediction-tutorial-with-eda\n3. https://www.kaggle.com/code/freespirit08/time-series-for-beginners-with-arima\n4. https://www.kaggle.com/code/robikscube/time-series-forecasting-with-prophet\n5. https://www.kaggle.com/code/iamleonie/intro-to-time-series-forecasting\n6. https://www.kaggle.com/code/robikscube/time-series-forecasting-with-machine-learning-yt\n7. https://www.kaggle.com/code/rohanrao/a-modern-time-series-tutorial\n8. https://www.kaggle.com/code/cdeotte/time-series-eda\n9. https://www.kaggle.com/code/janiobachmann/s-p-500-time-series-forecasting-with-prophet\n10. https://www.kaggle.com/code/robikscube/pt2-time-series-forecasting-with-xgboost\n\nWishing you the best for the assignment and happy learning!", "votes": null }, { "id": "3017764", "postDate": "10/15/2024 07:22:54", "content": "Thanks @ravi20076 This is ultra useful
", "rawMarkdown": "Thanks @ravi20076 This is ultra useful", "votes": null }, { "id": "3017774", "postDate": "10/15/2024 07:33:56", "content": "Much valuable resources. @ravi20076
", "rawMarkdown": "Much valuable resources. @ravi20076", "votes": null }, { "id": "3018163", "postDate": "10/15/2024 15:00:50", "content": "Thanks for sharing valuable resources @muhammedtausif
", "rawMarkdown": "Thanks for sharing valuable resources @muhammedtausif", "votes": null }, { "id": "3018179", "postDate": "10/15/2024 15:10:28", "content": "@nancyalaswad90 most welcome
", "rawMarkdown": "nancyalaswad90 most welcome", "votes": null }, { "id": "3018517", "postDate": "10/15/2024 20:12:18", "content": "Ravi, your resources have given me a fantastic start for the competition. I truly appreciate your support and the effort you put into sharing them. Your help has made a big difference, and I’m grateful for it!
", "rawMarkdown": "Ravi, your resources have given me a fantastic start for the competition. I truly appreciate your support and the effort you put into sharing them. Your help has made a big difference, and I’m grateful for it!", "votes": null }, { "id": "3018864", "postDate": "10/16/2024 05:11:19", "content": "Thanks for sharing
", "rawMarkdown": "Thanks for sharing", "votes": null }, { "id": "3018906", "postDate": "10/16/2024 05:53:22", "content": "Thanks for sharing It means a lot!
", "rawMarkdown": "Thanks for sharing It means a lot!", "votes": null }, { "id": "3018946", "postDate": "10/16/2024 06:54:47", "content": "Thanks for sharing such useful resources, you have been an inspiration for mr.
", "rawMarkdown": "Thanks for sharing such useful resources, you have been an inspiration for mr.", "votes": null }, { "id": "3018955", "postDate": "10/16/2024 07:03:54", "content": "Great resource for beginners . Thanks for sharing!
", "rawMarkdown": "Great resource for beginners . Thanks for sharing!", "votes": null }, { "id": "3019031", "postDate": "10/16/2024 07:53:49", "content": "Thanks a lot, I really love time series and this helps me learn more!!
", "rawMarkdown": "Thanks a lot, I really love time series and this helps me learn more!!", "votes": null }, { "id": "3019205", "postDate": "10/16/2024 11:02:48", "content": "Great! @mrishikesh45 this is my favourite area too!
", "rawMarkdown": "Great! @mrishikesh45 this is my favourite area too!", "votes": null }, { "id": "3019259", "postDate": "10/16/2024 11:47:13", "content": "Thank you for sharing!
", "rawMarkdown": "Thank you for sharing!", "votes": null }, { "id": "3019373", "postDate": "10/16/2024 14:21:33", "content": "this is a treasure! @ravi20076
", "rawMarkdown": "this is a treasure! @ravi20076", "votes": null }, { "id": "3019895", "postDate": "10/17/2024 02:28:27", "content": "Thank you man this its amazing!!!
", "rawMarkdown": "Thank you man this its amazing!!!", "votes": null }, { "id": "3019939", "postDate": "10/17/2024 03:17:34", "content": "Thank you for sharing these useful resources! I really appreciate the effort in providing helpful materials for this competition. @ravi20076
", "rawMarkdown": "Thank you for sharing these useful resources! I really appreciate the effort in providing helpful materials for this competition. @ravi20076", "votes": null }, { "id": "3020017", "postDate": "10/17/2024 05:25:06", "content": "Excellent resource for novices 👌
", "rawMarkdown": "Excellent resource for novices 👌", "votes": null }, { "id": "3020758", "postDate": "10/17/2024 21:19:39", "content": "Thanks for sharing!
", "rawMarkdown": "Thanks for sharing!", "votes": null }, { "id": "3021169", "postDate": "10/18/2024 08:33:58", "content": "The model is evaluated based on training data, although no specific evaluation metrics (e.g., RMSE or MAE) are highlighted in the notebook.
\nCross-validation may be beneficial to validate the robustness of the model across different subsets of the data.
We have a custom metric here - please peruse the overview page for details @sherriffasherriff
", "rawMarkdown": "We have a custom metric here - please peruse the overview page for details @sherriffasherriff", "votes": null }, { "id": "3021185", "postDate": "10/18/2024 08:43:59", "content": "Thanks for Sharing. Further exploration of feature engineering could improve model performance, such as adding time-based features or technical indicators relevant to financial forecasting.
", "rawMarkdown": "Thanks for Sharing. Further exploration of feature engineering could improve model performance, such as adding time-based features or technical indicators relevant to financial forecasting.", "votes": null }, { "id": "3021343", "postDate": "10/18/2024 11:51:43", "content": "Thank you for sharing with us like this very useful references and starter materials ! @ravi20076
", "rawMarkdown": "Thank you for sharing with us like this very useful references and starter materials ! @ravi20076", "votes": null }, { "id": "3021361", "postDate": "10/18/2024 12:09:50", "content": "Very useful, thanks very much Ravi!
", "rawMarkdown": "Very useful, thanks very much Ravi!", "votes": null }, { "id": "3022489", "postDate": "10/19/2024 16:12:03", "content": "I appreciate you sharing these helpful resources, @ravi2007
", "rawMarkdown": "I appreciate you sharing these helpful resources, @ravi2007", "votes": null }, { "id": "3022772", "postDate": "10/19/2024 23:32:52", "content": "Hi @ravi20076 , sorry to barge with a potentially unrelated to this particular thread question, but can you use your Kaggle GPU allowance? I have definitely verified my account (hence I can take part in this competition) but turning on an accelerator mode doesn't work, even with the likes of XGBoost with cuda enabled or LGB…
", "rawMarkdown": "Hi @ravi20076 , sorry to barge with a potentially unrelated to this particular thread question, but can you use your Kaggle GPU allowance? I have definitely verified my account (hence I can take part in this competition) but turning on an accelerator mode doesn't work, even with the likes of XGBoost with cuda enabled or LGB…", "votes": null }, { "id": "3022950", "postDate": "10/20/2024 05:42:00", "content": "You need to ask Kaggle this question. Please raise this in the product feedback section here @missgranger
\n
This seems to be an exhaustive set for time series competitions held on kaggle! Its actually amazing to see all of them stacked together in this post.
", "rawMarkdown": "This seems to be an exhaustive set for time series competitions held on kaggle! Its actually amazing to see all of them stacked together in this post.", "votes": null }, { "id": "3024287", "postDate": "10/21/2024 13:35:21", "content": "thanks for taking this effort !
", "rawMarkdown": "thanks for taking this effort !", "votes": null }, { "id": "3024822", "postDate": "10/22/2024 03:54:55", "content": "Thanks for sharing those excellent resources!
", "rawMarkdown": "Thanks for sharing those excellent resources!", "votes": null }, { "id": "3025137", "postDate": "10/22/2024 12:52:51", "content": "Loved the references
\nThanks for sharing! ^_^
A lot of great info here!
", "rawMarkdown": "A lot of great info here!", "votes": null }, { "id": "3027872", "postDate": "10/25/2024 11:41:08", "content": "Super helpful. Thanks a lot for sharing!
", "rawMarkdown": "Super helpful. Thanks a lot for sharing!", "votes": null }, { "id": "3028077", "postDate": "10/25/2024 15:39:59", "content": "Thanks, very useful!
", "rawMarkdown": "Thanks, very useful!", "votes": null }, { "id": "3029448", "postDate": "10/27/2024 10:32:59", "content": "Great info! Thanks for sharing with us
", "rawMarkdown": "Great info! Thanks for sharing with us", "votes": null }, { "id": "3029567", "postDate": "10/27/2024 13:10:14", "content": "Very useful. Thanks
", "rawMarkdown": "Very useful. Thanks", "votes": null }, { "id": "3030052", "postDate": "10/28/2024 05:36:49", "content": "Thanks for sharing such useful resources for Begineers…
", "rawMarkdown": "Thanks for sharing such useful resources for Begineers...", "votes": null }, { "id": "3030222", "postDate": "10/28/2024 10:09:10", "content": "This is really helpful, thanks for sharing.
", "rawMarkdown": "This is really helpful, thanks for sharing.", "votes": null }, { "id": "3031115", "postDate": "10/29/2024 11:15:10", "content": "Thanks a lot for sharing! Super useful content
", "rawMarkdown": "Thanks a lot for sharing! Super useful content", "votes": null }, { "id": "3032059", "postDate": "10/30/2024 13:59:54", "content": "Thanks a lot for sharing!
", "rawMarkdown": "Thanks a lot for sharing!", "votes": null }, { "id": "3032252", "postDate": "10/30/2024 18:04:02", "content": "Thanks for all your good wishes and appreciations. Wishing you the best for the competition!
", "rawMarkdown": "Thanks for all your good wishes and appreciations. Wishing you the best for the competition!", "votes": null }, { "id": "3032720", "postDate": "10/31/2024 09:33:17", "content": "Thanks for sharing
", "rawMarkdown": "Thanks for sharing", "votes": null }, { "id": "3033165", "postDate": "10/31/2024 20:25:47", "content": "Very useful list, thank you
", "rawMarkdown": "Very useful list, thank you", "votes": null }, { "id": "3041977", "postDate": "11/11/2024 03:50:33", "content": "Thanks for sharing! they`re really helpful
", "rawMarkdown": "Thanks for sharing! they`re really helpful", "votes": null }, { "id": "3064216", "postDate": "12/05/2024 11:28:36", "content": "thanks for sharing.
", "rawMarkdown": "thanks for sharing.", "votes": null }, { "id": "3080201", "postDate": "12/24/2024 21:08:24", "content": "Thanks for sharing! Seems pretty useful
", "rawMarkdown": "Thanks for sharing! Seems pretty useful", "votes": null } ], "comments": [ { "id": 3017764, "author_name": "guohansheng", "author_url": "", "post_date": "10/15/2024 07:22:54", "content": "Thanks @ravi20076 This is ultra useful
", "votes": null, "replies": [] }, { "id": 3017774, "author_name": "muhammedtausif", "author_url": "", "post_date": "10/15/2024 07:33:56", "content": "Much valuable resources. @ravi20076
", "votes": null, "replies": [] }, { "id": 3018163, "author_name": "nancyalaswad90", "author_url": "", "post_date": "10/15/2024 15:00:50", "content": "Thanks for sharing valuable resources @muhammedtausif
", "votes": null, "replies": [ { "id": 3018179, "author_name": "ravi20076", "author_url": "", "post_date": "10/15/2024 15:10:28", "content": "@nancyalaswad90 most welcome
", "votes": null, "replies": [] } ] }, { "id": 3018517, "author_name": "bruceliboy", "author_url": "", "post_date": "10/15/2024 20:12:18", "content": "Ravi, your resources have given me a fantastic start for the competition. I truly appreciate your support and the effort you put into sharing them. Your help has made a big difference, and I’m grateful for it!
", "votes": null, "replies": [] }, { "id": 3018864, "author_name": "jilsharma2079", "author_url": "", "post_date": "10/16/2024 05:11:19", "content": "Thanks for sharing
", "votes": null, "replies": [] }, { "id": 3018906, "author_name": "tanishkpatil", "author_url": "", "post_date": "10/16/2024 05:53:22", "content": "Thanks for sharing It means a lot!
", "votes": null, "replies": [] }, { "id": 3018946, "author_name": "nittsgh", "author_url": "", "post_date": "10/16/2024 06:54:47", "content": "Thanks for sharing such useful resources, you have been an inspiration for mr.
", "votes": null, "replies": [] }, { "id": 3018955, "author_name": "tahirc1", "author_url": "", "post_date": "10/16/2024 07:03:54", "content": "Great resource for beginners . Thanks for sharing!
", "votes": null, "replies": [] }, { "id": 3019031, "author_name": "mrishikesh45", "author_url": "", "post_date": "10/16/2024 07:53:49", "content": "Thanks a lot, I really love time series and this helps me learn more!!
", "votes": null, "replies": [ { "id": 3019205, "author_name": "ravi20076", "author_url": "", "post_date": "10/16/2024 11:02:48", "content": "Great! @mrishikesh45 this is my favourite area too!
", "votes": null, "replies": [] } ] }, { "id": 3019259, "author_name": "liangtianle", "author_url": "", "post_date": "10/16/2024 11:47:13", "content": "Thank you for sharing!
", "votes": null, "replies": [] }, { "id": 3019373, "author_name": "saurabhbadole", "author_url": "", "post_date": "10/16/2024 14:21:33", "content": "this is a treasure! @ravi20076
", "votes": null, "replies": [] }, { "id": 3019895, "author_name": "darwinberrio", "author_url": "", "post_date": "10/17/2024 02:28:27", "content": "Thank you man this its amazing!!!
", "votes": null, "replies": [] }, { "id": 3019939, "author_name": "rishantenis", "author_url": "", "post_date": "10/17/2024 03:17:34", "content": "Thank you for sharing these useful resources! I really appreciate the effort in providing helpful materials for this competition. @ravi20076
", "votes": null, "replies": [] }, { "id": 3020017, "author_name": "humayrakhanomrime", "author_url": "", "post_date": "10/17/2024 05:25:06", "content": "Excellent resource for novices 👌
", "votes": null, "replies": [] }, { "id": 3020758, "author_name": "haoyuewan", "author_url": "", "post_date": "10/17/2024 21:19:39", "content": "Thanks for sharing!
", "votes": null, "replies": [] }, { "id": 3021169, "author_name": "sherriffasherriff", "author_url": "", "post_date": "10/18/2024 08:33:58", "content": "The model is evaluated based on training data, although no specific evaluation metrics (e.g., RMSE or MAE) are highlighted in the notebook.
\nCross-validation may be beneficial to validate the robustness of the model across different subsets of the data.
We have a custom metric here - please peruse the overview page for details @sherriffasherriff
", "votes": null, "replies": [] } ] }, { "id": 3021185, "author_name": "sherriffasherriff", "author_url": "", "post_date": "10/18/2024 08:43:59", "content": "Thanks for Sharing. Further exploration of feature engineering could improve model performance, such as adding time-based features or technical indicators relevant to financial forecasting.
", "votes": null, "replies": [] }, { "id": 3021343, "author_name": "rishantenis", "author_url": "", "post_date": "10/18/2024 11:51:43", "content": "Thank you for sharing with us like this very useful references and starter materials ! @ravi20076
", "votes": null, "replies": [] }, { "id": 3021361, "author_name": "pranaybhaveshchauhan", "author_url": "", "post_date": "10/18/2024 12:09:50", "content": "Very useful, thanks very much Ravi!
", "votes": null, "replies": [] }, { "id": 3022489, "author_name": "narendrabirajdar", "author_url": "", "post_date": "10/19/2024 16:12:03", "content": "I appreciate you sharing these helpful resources, @ravi2007
", "votes": null, "replies": [] }, { "id": 3022772, "author_name": "missgranger", "author_url": "", "post_date": "10/19/2024 23:32:52", "content": "Hi @ravi20076 , sorry to barge with a potentially unrelated to this particular thread question, but can you use your Kaggle GPU allowance? I have definitely verified my account (hence I can take part in this competition) but turning on an accelerator mode doesn't work, even with the likes of XGBoost with cuda enabled or LGB…
", "votes": null, "replies": [ { "id": 3022950, "author_name": "ravi20076", "author_url": "", "post_date": "10/20/2024 05:42:00", "content": "You need to ask Kaggle this question. Please raise this in the product feedback section here @missgranger
\n
This seems to be an exhaustive set for time series competitions held on kaggle! Its actually amazing to see all of them stacked together in this post.
", "votes": null, "replies": [] }, { "id": 3024287, "author_name": "sahityasetu", "author_url": "", "post_date": "10/21/2024 13:35:21", "content": "thanks for taking this effort !
", "votes": null, "replies": [] }, { "id": 3024822, "author_name": "daffintw", "author_url": "", "post_date": "10/22/2024 03:54:55", "content": "Thanks for sharing those excellent resources!
", "votes": null, "replies": [] }, { "id": 3025137, "author_name": "christophercamilo", "author_url": "", "post_date": "10/22/2024 12:52:51", "content": "Loved the references
\nThanks for sharing! ^_^
A lot of great info here!
", "votes": null, "replies": [] }, { "id": 3027872, "author_name": "bwearryyu", "author_url": "", "post_date": "10/25/2024 11:41:08", "content": "Super helpful. Thanks a lot for sharing!
", "votes": null, "replies": [] }, { "id": 3028077, "author_name": "brunovolckaert", "author_url": "", "post_date": "10/25/2024 15:39:59", "content": "Thanks, very useful!
", "votes": null, "replies": [] }, { "id": 3029448, "author_name": "dinezra11", "author_url": "", "post_date": "10/27/2024 10:32:59", "content": "Great info! Thanks for sharing with us
", "votes": null, "replies": [] }, { "id": 3029567, "author_name": "unknowncom", "author_url": "", "post_date": "10/27/2024 13:10:14", "content": "Very useful. Thanks
", "votes": null, "replies": [] }, { "id": 3030052, "author_name": "datawarriors", "author_url": "", "post_date": "10/28/2024 05:36:49", "content": "Thanks for sharing such useful resources for Begineers…
", "votes": null, "replies": [] }, { "id": 3030222, "author_name": "pranshumeshram", "author_url": "", "post_date": "10/28/2024 10:09:10", "content": "This is really helpful, thanks for sharing.
", "votes": null, "replies": [] }, { "id": 3031115, "author_name": "sahil05thecodeguy", "author_url": "", "post_date": "10/29/2024 11:15:10", "content": "Thanks a lot for sharing! Super useful content
", "votes": null, "replies": [] }, { "id": 3032059, "author_name": "fastso", "author_url": "", "post_date": "10/30/2024 13:59:54", "content": "Thanks a lot for sharing!
", "votes": null, "replies": [] }, { "id": 3032252, "author_name": "ravi20076", "author_url": "", "post_date": "10/30/2024 18:04:02", "content": "Thanks for all your good wishes and appreciations. Wishing you the best for the competition!
", "votes": null, "replies": [] }, { "id": 3032720, "author_name": "suienishmirambekov", "author_url": "", "post_date": "10/31/2024 09:33:17", "content": "Thanks for sharing
", "votes": null, "replies": [] }, { "id": 3033165, "author_name": "gidoda", "author_url": "", "post_date": "10/31/2024 20:25:47", "content": "Very useful list, thank you
", "votes": null, "replies": [] }, { "id": 3041977, "author_name": "jwt1124", "author_url": "", "post_date": "11/11/2024 03:50:33", "content": "Thanks for sharing! they`re really helpful
", "votes": null, "replies": [] }, { "id": 3064216, "author_name": "billfan88", "author_url": "", "post_date": "12/05/2024 11:28:36", "content": "thanks for sharing.
", "votes": null, "replies": [] }, { "id": 3080201, "author_name": "m1gusta", "author_url": "", "post_date": "12/24/2024 21:08:24", "content": "Thanks for sharing! Seems pretty useful
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3017144": "Hello all,\n\nWishing you the best for the competition! This is my favourite data science topic, as a Finance post-grad and I am excited to witness such a competition after a long wait! Hope the below materials help you onboard well and efficiently-\n\n# [Past edition - Jane Street Market Prediction](https://www.kaggle.com/competitions/jane-street-market-prediction/overview)\n## Most voted kernels\n- https://www.kaggle.com/code/carlmcbrideellis/jane-street-eda-of-day-0-and-feature-importance\n- https://www.kaggle.com/code/hamditarek/market-prediction-xgboost-with-gpu-fit-in-1min\n- https://www.kaggle.com/code/aimind/bottleneck-encoder-mlp-keras-tuner-8601c5\n- https://www.kaggle.com/code/gogo827jz/jane-street-neural-network-starter\n- https://www.kaggle.com/code/muhammadmelsherbini/jane-street-extensive-eda-pca-starter\n- https://www.kaggle.com/code/tarlannazarov/own-jane-street-with-keras-nn\n- https://www.kaggle.com/code/odins0n/exploring-time-series-plots-beginners-guide\n- https://www.kaggle.com/code/jorijnsmit/found-the-holy-grail-grouptimeseriessplit\n- https://www.kaggle.com/code/snippsy/bottleneck-encoder-mlp-keras-tuner\n- https://www.kaggle.com/code/a763337092/blending-tensorflow-and-pytorch\n\n## Top ranked solutions\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224348 -- rank 1\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224713 -- rank 3\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/226837 -- rank 10\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/269181 -- rank 15\n- https://www.kaggle.com/competitions/jane-street-market-prediction/discussion/224079 -- rank 23\n\n# Similar Kaggle competition references\n### [ASHRAE - Great Energy Predictor III](https://www.kaggle.com/competitions/ashrae-energy-prediction)\n#### Kernels- top 5 voted \n1. https://www.kaggle.com/code/caesarlupum/ashrae-start-here-a-gentle-introduction\n2. https://www.kaggle.com/code/nroman/eda-for-ashrae\n3. https://www.kaggle.com/code/rohanrao/ashrae-half-and-half\n4. https://www.kaggle.com/code/corochann/optuna-tutorial-for-hyperparameter-optimization\n5. https://www.kaggle.com/code/corochann/ashrae-training-lgbm-by-meter-type\n\n#### Kernels- top 3 scores\n1. https://www.kaggle.com/code/patrick0302/postprocessed-models-bland-by-leak\n2. https://www.kaggle.com/code/vladimirsydor/add-leak\n3. https://www.kaggle.com/code/gpamoukoff/ashrae-subm-stack-fin-3\n\n### High scoring approaches and discussions- \n1. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124709 -- rank1\n2. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/123481 -- rank2\n3. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124984 -- rank3\n4. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/124788 --rank4\n5. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/127086 --rank5\n6. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/123525 -- rank9\n\n### Other useful posts and discussions\n1. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/112872\n2. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/114345\n3. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/113678\n4. https://www.kaggle.com/competitions/ashrae-energy-prediction/discussion/115698\n\n## [Optiver- Trading at the Close](https://www.kaggle.com/competitions/optiver-trading-at-the-close)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ravi20076/optiver-baseline-models\n2. https://www.kaggle.com/code/yuanzhezhou/baseline-lgb-xgb-and-catboost\n3. https://www.kaggle.com/code/a27182818/explain-the-data-lightgbm-baseline\n4. https://www.kaggle.com/code/lblhandsome/optiver-robust-best-single-model\n5. https://www.kaggle.com/code/verracodeguacas/fold-cv\n6. https://www.kaggle.com/code/peizhengwang/best-public-score\n7. https://www.kaggle.com/code/siddhvr/optiver-trading-at-the-close-sub\n\n### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/487446\n2. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/486868\n3. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/462653\n4. https://www.kaggle.com/competitions/optiver-trading-at-the-close/discussion/486086\n\n## [Enefit - Predict Energy Behavior of Prosumers](https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/rafiko1/enefit-xgboost-starter\n2. https://www.kaggle.com/code/vitalykudelya/explain-dataset-and-baseline\n3. https://www.kaggle.com/code/vincentschuler/enefit-baseline-cross-validation\n4. https://www.kaggle.com/code/greysky/enefit-generic-notebook\n5. https://www.kaggle.com/code/vitalykudelya/enefit-target-diff\n6. https://www.kaggle.com/code/ahmedabdulwahab/pandas-data-description-and-starters-guide\n\n### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472793\n2. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499938\n3. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499397\n4. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/499649\n5. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472537\n6. https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472537\n\n## Playground Time Series Forecasting challenges\n### [Season3-Episode 20](https://www.kaggle.com/competitions/playground-series-s3e20)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ambrosm/pss3e20-eda-which-makes-sense\n2. https://www.kaggle.com/code/kacperrabczewski/rwanda-co2-step-by-step-guide\n3. https://www.kaggle.com/code/yaaangzhou/pg-s3-e20-eda-modeling\n4. https://www.kaggle.com/code/dmitryuarov/ps3e20-rwanda-emission-advanced-fe-20-88\n5. https://www.kaggle.com/code/iqbalsyahakbar/ps3e20-time-series-for-beginners\n\n### [Season3-Episode 19](https://www.kaggle.com/competitions/playground-series-s3e19)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/tumpanjawat/s3e19-course-eda-fe-lightgbm\n2. https://www.kaggle.com/code/iqbalsyahakbar/ps3e19-time-series-for-beginners\n3. https://www.kaggle.com/code/ivyzang/1st-place-solution-less-is-more\n4. https://www.kaggle.com/code/tetsutani/ps3e19-eda-ensemble-ml-pipeline-rnn-by-skorch\n5. https://www.kaggle.com/code/kacperrabczewski/last-minute-forecasting\n\n### [TPS- September2022](https://www.kaggle.com/competitions/tabular-playground-series-sep-2022)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting\n2. https://www.kaggle.com/code/cabaxiom/tps-sep-22-eda-and-linear-regression-baseline\n3. https://www.kaggle.com/code/khashayarrahimi94/why-you-should-not-use-correlation\n4. https://www.kaggle.com/code/vencerlanz09/tps-eda-9-models-explanation\n5. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis\n\n### [TPS- January2022](https://www.kaggle.com/competitions/tabular-playground-series-jan-2022)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model\n2. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense\n3. https://www.kaggle.com/code/usharengaraju/tensorflow-tf-data-keraspreprocessinglayers-w-b\n4. https://www.kaggle.com/code/teckmengwong/tps2201-hybrid-time-series\n5. https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex\n\n## [GoDaddy - Microbusiness Density Forecasting](https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/overview)\n#### Kernels- most voted \n1. https://www.kaggle.com/code/titericz/better-xgb-baseline\n2. https://www.kaggle.com/code/cdeotte/linear-regression-baseline-lb-1-092\n3. https://www.kaggle.com/code/tanmay111999/gdmbf-ar-ma-arma-arima-sarima-auto-arima\n4. https://www.kaggle.com/code/cdeotte/seasonal-model-with-validation-lb-1-091\n5. https://www.kaggle.com/code/kimtaehun/complete-baseline-code-with-various-ml-model\n\n#### High scoring approaches and discussions-\n1. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/395131 -- rank1\n2. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/395264 -- rank2\n3. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/418287 -- rank3\n4. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/394821 -- rank4\n\n## [WiDS Datathon 2023](https://www.kaggle.com/competitions/widsdatathon2023)\n### Kernels- most voted \n1. https://www.kaggle.com/code/iamleonie/wids-datathon-2023-forecasting-with-lgbm\n2. https://www.kaggle.com/code/kooaslansefat/wids-2023-woman-life-freedom\n3. https://www.kaggle.com/code/khsamaha/eda-wids-datathon-2023-r\n\n## Miscellaneous discussion references on time series- \n1. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574\n2. https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/discussion/46602\n3. https://www.kaggle.com/competitions/demand-forecasting-kernels-only/discussion/63568\n4. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/133463\n5. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/135713\n6. https://www.kaggle.com/competitions/m5-forecasting-accuracy/discussion/134707\n\n## Miscellaneous kernel references on time series- \n1. https://www.kaggle.com/code/ravi20076/optiver-baseline-models\n2. https://www.kaggle.com/code/kanncaa1/time-series-prediction-tutorial-with-eda\n3. https://www.kaggle.com/code/freespirit08/time-series-for-beginners-with-arima\n4. https://www.kaggle.com/code/robikscube/time-series-forecasting-with-prophet\n5. https://www.kaggle.com/code/iamleonie/intro-to-time-series-forecasting\n6. https://www.kaggle.com/code/robikscube/time-series-forecasting-with-machine-learning-yt\n7. https://www.kaggle.com/code/rohanrao/a-modern-time-series-tutorial\n8. https://www.kaggle.com/code/cdeotte/time-series-eda\n9. https://www.kaggle.com/code/janiobachmann/s-p-500-time-series-forecasting-with-prophet\n10. https://www.kaggle.com/code/robikscube/pt2-time-series-forecasting-with-xgboost\n\nWishing you the best for the assignment and happy learning!", "3017764": "Thanks @ravi20076 This is ultra useful", "3017774": "Much valuable resources. @ravi20076", "3018163": "Thanks for sharing valuable resources @muhammedtausif", "3018179": "nancyalaswad90 most welcome", "3018517": "Ravi, your resources have given me a fantastic start for the competition. I truly appreciate your support and the effort you put into sharing them. Your help has made a big difference, and I’m grateful for it!", "3018864": "Thanks for sharing", "3018906": "Thanks for sharing It means a lot!", "3018946": "Thanks for sharing such useful resources, you have been an inspiration for mr.", "3018955": "Great resource for beginners . Thanks for sharing!", "3019031": "Thanks a lot, I really love time series and this helps me learn more!!", "3019205": "Great! @mrishikesh45 this is my favourite area too!", "3019259": "Thank you for sharing!", "3019373": "this is a treasure! @ravi20076", "3019895": "Thank you man this its amazing!!!", "3019939": "Thank you for sharing these useful resources! I really appreciate the effort in providing helpful materials for this competition. @ravi20076", "3020017": "Excellent resource for novices 👌", "3020758": "Thanks for sharing!", "3021169": "The model is evaluated based on training data, although no specific evaluation metrics (e.g., RMSE or MAE) are highlighted in the notebook.\nCross-validation may be beneficial to validate the robustness of the model across different subsets of the data.", "3021179": "We have a custom metric here - please peruse the overview page for details @sherriffasherriff", "3021185": "Thanks for Sharing. Further exploration of feature engineering could improve model performance, such as adding time-based features or technical indicators relevant to financial forecasting.", "3021343": "Thank you for sharing with us like this very useful references and starter materials ! @ravi20076", "3021361": "Very useful, thanks very much Ravi!", "3022489": "I appreciate you sharing these helpful resources, @ravi2007", "3022772": "Hi @ravi20076 , sorry to barge with a potentially unrelated to this particular thread question, but can you use your Kaggle GPU allowance? I have definitely verified my account (hence I can take part in this competition) but turning on an accelerator mode doesn't work, even with the likes of XGBoost with cuda enabled or LGB…", "3022950": "You need to ask Kaggle this question. Please raise this in the product feedback section here @missgranger \n\n", "3023616": "This seems to be an exhaustive set for time series competitions held on kaggle! Its actually amazing to see all of them stacked together in this post.", "3024287": "thanks for taking this effort !", "3024822": "Thanks for sharing those excellent resources!", "3025137": "Loved the references\nThanks for sharing! ^_^", "3025200": "A lot of great info here!", "3027872": "Super helpful. Thanks a lot for sharing!", "3028077": "Thanks, very useful!", "3029448": "Great info! Thanks for sharing with us", "3029567": "Very useful. Thanks", "3030052": "Thanks for sharing such useful resources for Begineers...", "3030222": "This is really helpful, thanks for sharing.", "3031115": "Thanks a lot for sharing! Super useful content", "3032059": "Thanks a lot for sharing!", "3032252": "Thanks for all your good wishes and appreciations. Wishing you the best for the competition!", "3032720": "Thanks for sharing", "3033165": "Very useful list, thank you", "3041977": "Thanks for sharing! they`re really helpful", "3064216": "thanks for sharing.", "3080201": "Thanks for sharing! Seems pretty useful" }, "source": "meta" }