Evolving Deep CNN LSTMs for Inventory Time Series Prediction
Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non stationary characteristics of inventory data, the models employ Long Short Term Memory (LSTM) to capture long temporal dependencies and Convolutional Neural Network (CNN) to learn the local trend features. However, designing optimal CNN LSTM network architecture and tuning parameters can be challenging and would require consistent human supervision. To automate optimal architecture searching of CNN LSTM, we implement three meta heuristics: a Particle Swarm Optimisation (PSO) and two Differential Evolution (DE) variants. Computational experiments on real world inventory forecasting problems are conducted to evaluate the performance of the applied meta heuristics in terms of evolved network architectures for obtaining prediction accuracy. Moreover, the evolved CNN LSTM models are also compared to Seasonal Auto regressive Integrated Moving Average (SARIMA) models for inventory forecasting problems. The experimental results indicate that the evolved CNN LSTM models are capable of dealing with complex nonlinear inventory forecasting problem.
2019 IEEE Congress on Evolutionary Computation (CEC)