The competition is open to post-graduate students studying in the fields of Statistics, Data science, and other related disciplines. The competition is to test the student's ability to analyze and model time series data and develop accurate forecasting models. This competition will provide students with a platform to showcase their talents and gain recognition in the field. It will also offer valuable networking opportunities and the chance to learn from leading experts in the field of time series modeling.

The ITMC is organized by an international committee of experts in the field of time series forecasting and is open to stduents and practitioners from around the world. The competition serves as a valuable opportunity for participants to gain exposure and learn about the latest developments in the field of time series forecasting. Participants in the competition are required to submit their forecasting models and predictions, which are then evaluated using a variety of metrics.

1. Eligibility: The International Time Series Modeling Competition is open to all post-graduate students studying in the fields of statistics,  data science, economics, and other related disciplines.

2. Submission of Abstract: Entries must be submitted in a specified format and must include a report describing the methods and techniques used, as well as a description of the data used. Entries must be submitted electronically via  Google Form by the specified deadline.

3. Data: All data submitted for the time series modeling competition must meet the following criteria: 

   • The data must be related to time series analysis and clearly labeled with the relevant time variable.

   • The data must be in a commonly used format, such as CSV or Excel.

   • The data must be cleaned and preprocessed, with any missing or inconsistent values clearly identified and handled appropriately.

   • The data should not have any identifiable information about individuals or any confidential information.

   • The data should be publicly available, or the participant should have obtained the necessary permissions to use it for the competition.

   • The data should have a coherent time period, it should not be too old or too recent.

   • The data should be balanced, not biased towards any particular category.

   • The data should be big enough and diverse to allow for proper analysis and modeling.

After submitting your abstract, the competition organizers will review it and notify you of the outcome. If your abstract is accepted, you will be invited to participate in the competition. Participants found to have submitted data that does not meet these criteria will be disqualified from the competition.

4. Modeling: The models developed by the participants should be based on time series analysis and forecasting techniques. Any form of plagiarism will not be tolerated and will result in disqualification.

5. Judging: Entries will be judged by a panel of experts in the field of time series modeling. The judges' decisions will be final and binding.

6. Prizes: Prizes will be awarded to the top-performing participants. The winners will get the prize items through courier services.

7. Intellectual Property: By submitting an entry, participants agree to grant the organizer of the competition an irrevocable, non-exclusive, royalty-free license to use their entries for any purpose related to the competition.

8. Limitation of Liability: The organizer of the competition will not be liable for any loss or damage incurred by participants as a result of their participation in the competition.

9. Compliance with Law: Participants are responsible for ensuring that their participation in the competition complies with all applicable laws and regulations.

10. Changes to Terms and Conditions: The organizer of the competition reserves the right to modify these terms and conditions at any time without notice.

 Note: These topics are just suggestions and the competition  participant  team can choose to use any other topic that is relevant to the competition.

Reporting Format 

1. Introduction: This section should provide a brief overview of the competition, the dataset, and the problem being solved.

2. Data preprocessing: This section should describe the methods used to clean, transform and prepare the data for modeling.

3. Methods: This section should describe the techniques used to analyze and model the data. It should include details on the type of model used, the parameter chosen, and any other relevant information.

4.  Results: This section should present the results of the model in the form of tables, figures, and visualizations. It should also include a discussion about the performance of the model, making use of metrics such as accuracy, precision, recall or any other relevant measures.

5. Conclusion: This section should summarize the main findings of the analysis, and provide recommendations for future work.

6. References: This section should list any sources used in the report, including any papers, articles, or books that were consulted.

7. Appendices: This section should include any additional material that would be helpful for understanding the analysis, such as code snippets, data, or images.

Note that the report format may vary depending on the participants' choice, but generally should be clear, concise, and easy to understand for the reader. It should also include the technical details, along with the interpretation of the results, and the limitations and future work that can be done.