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Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior

Computer Science

Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior

A. T. Karl, J. Wisnowski, et al.

Discover groundbreaking insights as Andrew T Karl, James Wisnowski, and Lambros Petropoulos delve into the power of recurrent neural networks for accurately forecasting call center volumes. With a focus on overcoming complex seasonal patterns and autocorrelation, this research contrasts advanced deep learning techniques with traditional forecasting methods, revealing practical strategies for real-world applications.

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Playback language: English
Abstract
This paper investigates the applicability of recurrent neural networks (RNNs) in forecasting call center volumes, focusing on the challenges of complex seasonal patterns and autocorrelation. The authors compare the performance of Elman, LSTM, and GRU RNNs against traditional methods like Winters' smoothing and ARIMA, using a designed experiment and real-world call center data. The study explores parameter optimization and convergence criteria for RNNs and highlights the practical considerations for using deep learning in forecasting.
Publisher
Not specified in the provided text
Published On
Jan 01, 2023
Authors
Andrew T Karl, James Wisnowski, Lambros Petropoulos
Tags
recurrent neural networks
call center forecasting
seasonal patterns
Elman
LSTM
GRU
ARIMA

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