讲解：QBUS6840、Python、Python、Canvas

2019S1 QBUS6840 Group Project Page 1 of 5QBUS6840 Assignment 2 – Group Assignment:Due dates: Friday 24 May 2019Value: 30%RationaleThis assignment has been designed to help students develop valuable communication andcollaboration skills and to allow students to apply their predictive analytics skills on a realworld datasets.DescriptionThe assignment will be done in groups of 5 (or 4 or 6 depending on the total number ofstudents in the class) without exception. The group can be formed freely or assigned by theCoordinator. Please get close contact with your members in earlier stage. A group leader willbe automatically assigned by the Canvas system.Notes1. The assignment will be done in groups of 5 (or 4 or 6 depending on the total of studentsin the class) without exception. The group can be formed freely or assigned by theCoordinator. Please get close contact with your members in earlier stage. A groupleader for each group shall be automatically assigned on Canvas.2. The assignment is due at Friday 16:00pm 24 May 2019. The late penalty for theassignment is 5% of the assigned mark per day, starting after 16:00 pm on the due date.The closing date, 31 May 2019, 16:00pm is the last date on which an assessment willbe accepted for marking.3. Your answers shall be provided as a word-processed. Prepare one single report. Do nothave separate report for each question/task. Add your Python code as appendix to thereport. At the same time, we will ask you to upload your python code to your Canvasfolder.4. Your report should include the Group ID and SIDs of all group members. No names!You may stay with the report cover sheet provided.5. You need to provide full explanation and interpretation of any results you obtain.Output without explanation will receive zero marks.6. Be warned that plagiarism between individuals is always obvious to the markers of theassignment and can be easily detected by Turnitin.7. The data sets for this assignment can be downloaded from Canvas.8. Presentation of the assignment is part of the assignment. Markers will assign up to 10%of the mark for clarity of writing and presentation. It is recommended that you shouldinclude your Python code as appendix to your report, however you may insert smallsection of your code into the report for better interpretation when necessary. Thinkabout the best and most structured way to present your work, summarise the proceduresimplemented, support your results/findings and prove the originality of your work.9. Numbers with decimals should be reported to the Second decimal point.2019S1 QBUS6840 Group Project Page 2 of 510. The report should be NOT more than 25 pages, with font size no smaller than 11pt,including everything like text, figure, tables, small sections of inserted codes etc butexcluding the cover pages, appendix containing Python code and the meeting minutes.A violation of this rule will attract a penalty of 5% marks.Meeting Minutes1. Your group is required to submit meeting minutes, which are to be attached to thereport as the second appendix. Your group may use the attached templates for preparingagendas and meetings minutes.2. You should document at least 3 meeting minutes for this group assignment, using thetemplate provided/or a template you choose. Each minutes should at least record thefollowing information:a. Meeting dates/time/venue/duration;b. Key points of the process of discussion such as who said what (key points);c. Action list and responsible members, task due time etcd. Review/group judgement on the quality of individually completed/responsibletasks; The purpose of this is to infer whether a member is doing their share ofjobsNote: Any unsatisfactory meeting minutes may attract a penalty of up to 15% ofthe total marks.3. In case of a problem within a group we will request minutes of the previous meetings.We can make an individual adjustment to the group mark if there is sufficient evidencethat a student has done very little. If the student has truly done little, we will award amark of 0.Peer Assessment, Marks and Feedback1. We may ask for peer assessment from each student. The instruction how to do this willbe released later on.2. Each group will be awarded a group mark per the marking criteria. In some cases,individual marks may be applied if there is dispute in a group and the quality orquantity of contributions made by individuals are significantly different, in which casesthe unit coordinator will seek peer assessments reports from individuals in a group andmeeting minutes.3. We will allocate 15% marks for competition among the groups. The group with thehighest test score will secure full 15% marks while other groups will secure a markaccording to their test score against the best test score.4. Feedback will be provided on the marked submission.Background and DatasetThe S&P/ASX 200 index is used as the benchmark for Australian equity performance. It is amarket-capitalisation weighted and adjusted stock market index, see Wikipedia. ASX 200index is calculated based on the 200 largest ASX listed stocks. It starts from 31 March 2000with a value of 3133.3The ASX200 historical and current data can be downloaded fromhttps://au.finance.yahoo.com/quote/%5EAXJO?p=^AXJO. The historical data can bedownloaded at three different frequencies of Daily, Weekly and Monthly between any 2019S1 QBUS6840 Group Project Page 3 of 5specified duration since 31 March 2000. In this project, your group will be asked to analyzethe data at both frequency of daily and monthly. To align with the due date of the project, thefollowing strategy is recommended:1. Use 31 March 2000 as the starting date for downloading2. The end date can be set as a date when your group starts work on the project. Youmay explore this dataseQBUS6840作业代做、Python编程作业调试、Python课程作业代写、Canvas system作业代做 代写Pt and do all the possible tasks for the project.3. The Project due date is 24 May 2019. You can download the most recent full dataseton 23 May 2019. You may use this dataset to finalise your best model and make allthe predictions. Even the data on 24 May 2019 may become available before yousubmit your report, you shall not use it at all.4. For the group(s) who may be granted extension due to unforeseen reasons, thecompetition forecasts will be the next FIVE days from the granted due date.The dataset shall contain information of dates (daily or months), open price, highest price,lowest price, close price and adjusted close price. Your work in this project is to analyze thetime series of close price. A set of Daily and Monthly datasets have been downloaded foryour convenience if you dont wish to download it at early stage. You can get them directlyfrom Canvas.TasksPlease note most tasks are deliberately designed open. This gives more freedom for you toexplore a better solution.Data Pre-processing: Conduct initial analysis over the entire data. Write python program toclean up the data, e.g., checking/deleting incomplete information if any, to make sure data iscomplete, or normalising the data, etc. It is up to you how to normalise or transform the dataso that the resulting dataset can be well incorporated in training your chosen model(s). YouMUST retain your python program (or code section) used for all the pre-processing work.Exploratory Time Series: Analyze the entire time series for both daily and monthly data.You may plot them or do what you can to reveal any patterns. Summarise what you haverevealed or observed. In your report, carefully present your analysis and findings. Anydifferent patterns between Daily and Monthly time series?Benchmark Model: Based on what you have found from exploring the time series, considerusing a classical model to build your benchmark model for forecasting. This could be themoving average, or decomposition method, exponential moving average etc. It is always agood idea to split the given time series data into a training subset and a validation subset.Document your findings and justification. This should be done for both Daily and Monthlytime series.Build Advanced Models: You are requested to build at least TWO advanced models such asARIMA, State Space Model, Deep Neural Networks and Recurrent Neural Networks etc.This is your choice. In building your chosen models, you need to at least optimise models interms of e.g. the orders in ARIMA or State Space Model, and/or other parameters as well.Simply building a model without any consideration of validation and tuning hyperparameter 2019S1 QBUS6840 Group Project Page 4 of 5does not meet the minimal requirement for this task. Document your findings andjustification. This should be done for both Daily and Monthly time series.Competent Model and Final Result(s): Finally, according to your work, decide your bestmodel for both Daily and Monthly time series. For all of them, please make five forecastsahead. For the monthly time series, you may report your five forecast in a table in yourreport, but we ask you to forecast five daily results (i.e., the forecasts for Monday 27 May toFriday 31 May 2019) and save your results into a csv file containing two columns, one for thedate (named Dates) and the other column for the predicted values with the second decimalpoint. Name your file as GroupXXX_Results_ASX200.csv. where XXX is your groupnumber in form of e.g., 008 (for Group 8), 085 (for Group 85) and 123 (for Group 123). Theresults will be assessed against the actual close prices on the dates in order to decide yourgroup performance among the entire class (competition!). It is important for you to nameyour csv file in the above format, otherwise our program may fail your results.Note:1. The score will be based on the mean squared error.2. The close price on 24 May 2019 may become available, but you shall not use thatinformation to train you model. In the forecasting stage, for some models, you mayneed predict the close price on 24 May 2019, then predict close prices for the nextfive days based on the prediction for 24 May 2019. However, the forecast for 24 May2019 will not be used for assessment in competition.Presentation Please submit your project through the electronic system on Canvas The assignment material to be handed in will consist of a final report that:i) Takes a research article form in which you shall have a number of sectionssuch as introduction, methodology, experiment results, findings/interpretation,and conclusion. All references should be properly cited and take a fullbibliographical format. Here are couple of exampleshttp://cs229.stanford.edu/proj2015/007_report.pdfhttp://cs229.stanford.edu/proj2015/188_report.pdfhttp://cs229.stanford.edu/proj2015/031_report.pdfii) Details ALL steps and decisions taken by the group regarding requirementsabove.iii) Demonstrates an understanding of the relevant principles of predictiveanalytics approaches used.iv) Clearly and appropriately presents any relevant graphs and tables. The MAXIMUM page limit is 25 pages, including any computer output, graphs,tables, etc.2019S1 QBUS6840 Group Project Page 5 of 5 Your group is required to submit meetings minutes. Your group may use the attachedtemplates for preparing agendas and meetings minutes. You should document at least3 meetings during the semester. Documentation should be in terms of attendance,discussion points, actions decided, review etc. You may use your own form or findsomething online. You, as a member of a group, may be also required to submit your peer assessment.Please use the attached criteria sheet and assessment form for this purpose. You willbe informed of how to use online form when it becomes available. 转自：http://www.7daixie.com/2019051221714017.html