چکیده مقاله
Active customer forecasting is crucial for software as a service SaaS companies to plan resources and understand customer dynamics This study benchmarks sequence modeling approaches to predict active customer accounts using a real world dataset It evaluates hybrid recurrent and convolutional neural networks against Facebook Prophet models, focusing on both multivariate and univariate time series analysis Advanced models like Long Short Term Memory LSTM networks and 1D Convolutional Neural Networks Conv1D are utilized A comprehensive preprocessing pipeline and Bayesian hyperparameter optimization ensure robust models The multivariate LSTM Conv1D model shows superior performance, with a test mean absolute error of 16 49 and an R squared of 0 84, outperforming Prophet models and univariate LSTM Conv1D The hybrid deep learning architecture excels by incorporating multiple related time series and modeling their complex interactions The study finds up to a 96 62% improvement in accuracy over the Prophet model, highlighting deep learning's capability to capture intricate customer dynamics This research provides practical guidelines for effective nonlinear time series modeling in customer forecasting for SaaS companies, enhancing data driven decision making
کلیدواژهها
نویسندگان
Alireza Dehghan
Department of Industrial Engineering Sharif University of Technology Tehran, Iran
Moslem Habibi
Department of Industrial Engineering Sharif University of Technology Tehran, Iran
شیوه ارجاع
Dehghan, Alireza and Habibi, Moslem,1403,Time Series Forecasting of Active Customers Using Sequence Models: A Comparative Evaluation,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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