Joint Prediction of Multiple Waiting Times with Recurrent Neural Nets

需求预测对零售商来说一直很重要，因为它推动了有关商业战略和供应链管理的重要决策。传统方法使用经典统计模型对购买到达率进行建模，这些模型是在无法轻松访问可扩展计算能力以及广泛（许多类型的数据）和长期（对于许多人）购买数据的情况下开发的。近年来大量数据的涌入带来的好处不应仅限于更好地估计总体统计数据以应用经典统计模型，还应包括基于详细的个人层面购买信息的更细粒度建模。经典统计学中已经提出了许多模型来描述事件到达的行为，因为它们在许多领域都很有用，例如监测设备的剩余寿命和灾害预报。这些模型已经过精心描述，并且它们的属性已经过严格证明。然而，这些相同的模型也需要大量的假设，这些假设在现实中不一定成立。一个这样的假设是，在时间上相隔较远的事件之间的依赖性也往往较小。我们想探索一个数据驱动的模型，旨在放松这些假设。最近的爆炸在accessibilityto计算能力重新点燃了兴趣，人工神经网络，这已经显示出成功的许多不同领域的应用。对此的修改是递归神经网络。通过增加一个内部状态变量，这种神经网络也可以应用于序列数据。它也被证明是图灵完备的，这意味着它可以模拟任何给定足够大的参数集的计算机程序。RNN在自然语言处理中的应用已经相当成功，这表明该模型在捕获复杂的序列依赖方面比经典统计模型要好得多，因为经典模型需要短程依赖假设才能工作。

Demand forecasting has always been important for retailers as it drives important decisions regarding businessstrategy and supply chain management. Traditional approaches model the purchase arrival rates using classicalstatistical models, which were developed without easy access to scalable computing power and both wide(many types of data) and long (for many individuals) purchase data. Benefits brought forth by the huge influxof data during the recent years ought not to be limited to having better estimates of aggregate statistics to applyclassical statistical models, but should also include a more fine-grained modelling based on the detailedindividual-level purchase information.There have been many models proposed in classical statistics to describe the behaviour of event arrivals, asthey're useful in many areas, such as monitoring equipment remaining lifetimes and disaster forecasting. Thesemodels have been meticulously described and their properties have been rigorously proven. However, thesesame models also require a large number of assumptions, which may not necessarily hold in reality. One suchassumption is that the dependence between events occurring further apart in time also tends to be smaller. We'dlike to explore a data-driven model that aims to relax these assumptions. The recent explosion in accessibilityto computational power has re-ignited interest in the Artificial Neural Net, which has shown success in manydifferent areas of application. A modification to this is the Recurrent Neural Net. With the addition of aninternal state variable, this neural-network set-up can be applied to sequential data as well. It had also beenshown to be Turing complete, meaning that it can emulate any computer program given a large enough set ofparameters. Applications of the RNN in natural language processing have been rather successful,demonstrating that this model is much better than classical statistical models in capturing complex sequentialdependence, since the classical models require short-range dependence assumptions to work.