【发布时间】：2017-03-03 19:22:37
【问题描述】：
我支持客户按月为他们使用的各种服务付费的业务。我想根据客户对各种服务的历史使用情况使用机器学习，并预测未来的使用情况（增加或减少）。
我使用两个类创建了一个模型，它使用历史第 1 个月的服务使用情况和第 0 个月的使用情况来预测增长或下降。但我想开始使用所有历史信息，而不仅仅是 m-1。
我怎么能这样做？我可以选择继续添加 (M-2,M-3,M-4) 列吗？如果是这样，我将有数百列。
我是机器学习的新手，我不确定哪种算法最适合我正在做的分析类型。
这是我拥有的原始表格的示例：
Customer Name | MonthName | Service | Usage
------------- | ---------------|---------|------
Customer1 | January, 2017 |Service2 |$400
Customer1 | January, 2017 |Service1 |$300
Customer1 | January, 2017 |Service3 |$0
Customer1 | December, 2017 |Service2 |$600
Customer1 | December, 2017 |Service1 |$500
Customer1 | December, 2017 |Service3 |$700
Customer1 | November, 2016 |Service1 |$500
Customer1 | November, 2016 |Service2 |$50
Customer1 | October, 2016 |Service1 |$800
Customer2 | January, 2017 |Service2 |$400
Customer2 | January, 2017 |Service1 |$800
Customer2 | December, 2017 |Service2 |$600
Customer2 | December, 2017 |Service1 |$500
Customer2 | November, 2016 |Service1 |$500
Customer2 | November, 2016 |Service2 |$50
Customer2 | October, 2016 |Service1 |$800
这是我现在用来提出 2 类模型的表格：
+----------------+------------------+-----------------+-----------------+-----------------+-----------+-----------+-----------+-----------+-------+--------------------+
| Customer Name | MonthName | Service1 - M-1 | Service2 - M-1 | Service3 - M-1 | Usage M-1 | Service1 | Service2 | Service3 | Usage | Usage Decline Flag |
+----------------+------------------+-----------------+-----------------+-----------------+-----------+-----------+-----------+-----------+-------+--------------------+
| Customer1 | October, 2016 | 0 | 0 | 0 | 0 | 800 | | | 800 | 0 |
| Customer1 | November, 2016 | 800 | | | 800 | 500 | 50 | | 550 | 1 |
| Customer1 | December, 2017 | 500 | 50 | | 550 | 500 | 600 | 700 | 1800 | 0 |
| Customer1 | January, 2017 | 500 | 600 | 700 | 1800 | 300 | 400 | 0 | 700 | 1 |
| Customer2 | October, 2016 | 0 | 0 | 0 | 0 | 1600 | | | 1600 | 0 |
| Customer2 | November, 2016 | 1600 | | | 1600 | 500 | 100 | | 600 | 1 |
| Customer2 | December, 2017 | 500 | 100 | | 600 | 500 | 600 | | 1100 | 0 |
| Customer2 | January, 2017 | 500 | 600 | | 1100 | 800 | 400 | | 1200 | 0 |
+----------------+------------------+-----------------+-----------------+-----------------+-----------+-----------+-----------+-----------+-------+--------------------+
【问题讨论】：
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这不是本质上的“时间序列”学习吗（所以您有每个客户的数据序列并希望及时预测“下一个价值”）？
标签： azure machine-learning prediction azure-machine-learning-studio