September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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数据科学的应用场景
｜
49
2.4
需求预测
需求预测用在许多领域来预测诸如用电量、供应链库存、呼叫中心人员配备、现金
流规划、病床使用和许多其他使用场景。预测是一个时间序列问题，可以使用诸
如自回归综合移动平均线（
Auto-Regressive Integrated Moving Average
）、误差
-
趋势
-
季节性（
Error Trend Seasonality
）、非参数时间序列（
Non-Parametric Time
Series
）、
Prophet
和
DeepAR++
这样的流行算法解决。
从简单的电子表格到复杂的时间序列软件，企业使用任何东西来预测未来的业务成
果，比如产品需求、资源需求或财务绩效。这些方式通常使用历史的时间序列数据，
基于“过去的活动决定未来的需求”的假设来构建预测模型。这些纯粹使用时间序
列的方式难以在不规律的趋势和模式下生成准确的预测。这些方式通常不考虑和预
测相关的重要元数据，比如产品价格、产品类别和商店地址。
高估的预测会导致过度供应资源却不能充分利用，这会降低效率并增加开销。低估
的预测会导致必要资源供给不足并需要高开销的替代方案（比如支付加班费），从
而导致顾客满意度的降低和营收的减少。
一个有效的需求预测系统具有以下特点：
分析复杂关系而非仅限于时间序列数据
将时间序列数据与其他元数据结合，例如产品特点和商店位置。
将预测时间从月减少到小时
在自动加载、检查并识别数据集的关键属性之后，系统会迅速训练、优化并部
署适合于当前数据集的定制模型。
为许多不同使用场景创建预测
为几乎每个使用场景构建预测 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.