论文标题
ATM现金需求在印度银行有混乱和深度学习
ATM Cash demand forecasting in an Indian Bank with chaos and deep learning
论文作者
论文摘要
本文建议在ATM现金提取时间序列中建模混乱，并使用深度学习方法预测提款。它还认为每周的重要性，并将其作为虚拟外源变量。我们首先通过使用滞后重建每个系列的状态空间并使用自动相关函数和CAO的方法来重建每个系列的状态空间，从而建模了撤回时间序列中存在的混乱。此过程将Uni-Variate时间序列转换为多变量时间序列。一周的编码帮助“每周”将转换为七个功能。然后将这七个功能扩展到多元时间序列。 For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network.我们考虑了来自印度商业银行的每日现金提取数据集。在对混乱进行建模并在数据集中添加外源特征之后，我们观察到了所有模型的预测改善。尽管随机森林（RF）产生了更好的对称平均绝对百分比误差（SMAPE）值，但基于t检验，与RF相比，与RF相比，深度学习算法，即LSTM和1D CNN的性能相似。
This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.
