本次美国代写主要为时间序列相关的assignment
描述：
该作业将涵盖时间序列分类的概念
要求：
学生必须提交两个文档1）.ipynb文件2）从笔记本创建的.pdf或.html文件。
用降价单元格标记每个问题和子问题。在降价单元格中回答概念性问题。在代码块中注释的答案将不被视为功劳。
一字或一句话的答案将不会得到满分。
指示：
使用sktime内置函数加载acsf1数据集。 Docs（链接到外部网站。）
创建X_train，y_train和X_test，y_test
第1部分：35分了解数据集
在完整的UCR数据存储库中找到数据集描述。链接（链接到外部网站。）
7分根据acsf1详细网页（而不是摘要存储库页面）上的信息对数据集进行口头描述。
7分每次观察有1460个时间步长。使用len（）将其显示为X_train中的任何观察结果
7pts返回y_train中的类计数
7分绘制每个类的第一个时间序列（.iloc [0]），并用其指定的类名标记每个图。这些模式应与在acsf1详细网页上可以找到的模式匹配。
7分每次观察相距10秒。描述该图显示的第3、8和9类的内容。对这三个类中的每一个可能代表的设备给出一些直觉。
第2部分：15分时间序列分类模型的描述
5分选择一种分类模型类型。描述模型如何工作。为什么每个都适合这种类型的数据？
5分选择第二个分类模型类型。描述模型如何工作。为什么每个都适合这种类型的数据？
5分选择第三分类模型类型。描述模型如何工作。为什么每个都适合这种类型的数据？
第3部分：50分选择一种方法。建模并检查结果
10分仅选择一种方法。拟合您的模型。随意调整参数或尝试网格搜索（可选）
10分返回训练集和测试集的准确性得分（建议使用.score（））。打印测试集的混淆矩阵和分类报告。
10分讨论第8类的精度得分。从一维绘图以及混淆矩阵中获得您的视觉支持，以支持这一点。
10分讨论第5类的回忆得分。使用一维绘图以及混淆矩阵的视觉见解来支持这一点。
10分在以下业务案例中，您认为哪种指标最重要：您在本地电力供应商ComEd工作。您领导一个部门，该部门使用分析来计划芝加哥电网的电力供应。假设您的部门以固定的低费率预算一定数量的电力供应。如果芝加哥的总需求保持在购买的供应水平之内，则您的部门正在执行。如果需求超出此供应水平，则该公司将受到处罚，您的供应率将乘以100倍，从而破坏您部门的绩效。如果您必须建立预测模型以对高用电量（电器，空调，热水）与低用电量（照明，电视，电话充电器）的模式进行分类，您将使用哪个指标（精确度或召回率）？
Description:
This assignment will cover concepts of Time Series Classification
Requirements:
Students must submit TWO documents 1) .ipynb file 2) either .pdf or .html file created from the notebook.
Label each question and sub question with a markdown cell. Answer conceptual questions in a markdown cell. Answers that are commented in code blocks will not be considered for credit.
One word or one sentence answers will not receive full credit.
Instructions:
Load the acsf1 dataset using sktime built in functions. Docs (链接到外部网站。)
Create X_train, y_train and X_test, y_test
Part 1: 35 pts Understanding the dataset
Locate the dataset description within the full UCR data repository. Link (链接到外部网站。)
7pts Give a verbal description of the dataset from information on the acsf1 detailed webpage, not the summary repository page.
7pts There are 1460 time steps in each observation. Use len() to display this for any observation in the X_train
7pts Return the counts of classes in y_train
7pts Plot the first time series for each class(.iloc[0]), label each plot with its specified class name. The patterns should match what can be found on the acsf1 detailed webpage.
7pts Each observation is 10 seconds apart. Describe what the plots show for classes 3, 8 and 9. Give some intuition about what appliance each of these three classes might represent.
Part 2: 15 pts Description of Time Series Classification models
5 pts Select one classification model type. Describe how the model works. Why would each be a good or bad fit for this type of data?
5 pts Select a second classification model type. Describe how the model works. Why would each be a good or bad fit for this type of data?
5 pts Select third classification model type. Describe how the model works. Why would each be a good or bad fit for this type of data?
Part 3: 50 pts Select one method. Model and examine results
10 pts Select only one method. Fit your model. Feel free to adjust parameters or try a grid search (optional)
10 pts Return the accuracy score of the train set and test set (suggestion to use .score()). Print the confusion matrix and classification report of the test set.
10 pts Discuss the precision score for class 8. Support this with your visual opinion from plots in 1D as well as the confusion matrix.
10 pts Discuss the recall score for class 5. Support this with your visual opinion from plots in 1D as well as the confusion matrix.
10 pts Which metric do you feel is the most important in the following business case: You work for ComEd, a local electricity supplier. You head a department that uses analytics to plan electrical supply for Chicago’s power grid. Assume that your department budgets for a certain amount of electrical supply at a fixed low rate. If the total demand in Chicago stays within the purchased supply levels, your department is performing. If the demand breaches this supply level, the company is penalized and the rate for your supply multiplies by 100x, destroying your department’s performance. If you had to build your forecast model to classify patterns of high electrical usage (appliances, air conditioning, water heating) vs low electrical usage (lighting, tv, phone chargers) which metric (precision or recall) would you use?