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Article overview
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Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior | | Andrew T. Karl
; James Wisnowski
; Lambros Petropoulos
; | | Date: |
4 Jan 2023 | | Abstract: | Deep learning methods have gained popularity in recent years through the
media and the relative ease of implementation through open source packages such
as Keras. We investigate the applicability of popular recurrent neural networks
in forecasting call center volumes at a large financial services company. These
series are highly complex with seasonal patterns - between hours of the day,
day of the week, and time of the year - in addition to autocorrelation between
individual observations. Though we investigate the financial services industry,
the recommendations for modeling cyclical nonlinear behavior generalize across
all sectors. We explore the optimization of parameter settings and convergence
criteria for Elman (simple), Long Short-Term Memory (LTSM), and Gated Recurrent
Unit (GRU) RNNs from a practical point of view. A designed experiment using
actual call center data across many different "skills" (income call streams)
compares performance measured by validation error rates of the best observed
RNN configurations against other modern and classical forecasting techniques.
We summarize the utility of and considerations required for using deep learning
methods in forecasting. | | Source: | arXiv, 2301.01476 | | Services: | Forum | Review | PDF | Favorites | |
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