- 期刊
- OpenAccess
天气驱动行业销售大数据
Weather drive industry sales of big data
摘要
自建国以来我国的气象系统已经十分完备，2015 年中国气象局发布27 号令后使得气象数据迈向开放数据（Open Data）新阶段，行业与公众可以使用海量气象数据助力企业，目前行业数据和海量气象数据还没有得到完全应用。本文主要研究应用气象数据对销售的影响，进而利用气象数据特性完成天气驱动行业销售的预测。我们以两个零售行业的销售数据为例，结合气象局提供的天气数据进行分析。同时我们在分析中加入了经济因素，如上证指数和CPI 数据来提供外部环境支持。与传统的预测不同，在气象数据中，我们不仅知道目前时间点的数据，也有目前公众唾手可得的未来七天精确天气预报。我们采用目前流行的机器学习算法随机森林来建模，得到了很好的泛化结果。我们的预测模型可以解决销售行业传统通过从业人员的主观判断进行销售预测的局限，利用大数据分析实现更加精确可靠的指导。
關鍵字
無資料並列摘要
Since the founding of our country's meteorological system has been very complete, the China Meteorological Administration released in 2015 after making 27 orders meteorological data towards open data (Open Data) a new stage, with the public sector can use meteorological information massive help enterprises, industries and mass data meteorological data has not been fully applied. This paper studies the impact on sales meteorological data, weather data and then use feature complete weather-driven industry sales forecast. We have two retail industry sales data, for example, combined with weather information provided by the Bureau of Meteorology analysis. We joined the economic factors in the analysis, as the Shanghai Composite Index and CPI data to provide external environment support. Different from traditional forecasting, meteorological data, we not only know the information at present point in time, there are at present readily available public accurate weather forecast for the next seven days. We use the popular machine learning algorithms to model the random forest, the result has been very good generalization. Our forecast model can solve the traditional retail industry sales forecast by practitioners of the limitations of subjective judgment, the use of big data analysis to achieve a more accurate and reliable guidance.