An optimized model using LSTM network for demand forecasting

By Hossein Abbasimehr, M. Shabani, M. Yousefi. Computers & industrial engineering, 2020

502 citations recorded by the source corpus.

Abstract In a business environment with strict competition among firms, accurate demand forecasting is not straightforward. In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data. Therefore, in this paper we propose a demand forecasting method based on multi-layer LSTM networks. The proposed method automatically selects the best forecasting model by considering different combinations of LSTM hyperparameters for a given time series using the grid search method. It has the ability to capture nonlinear patterns in time series data, while considering the inherent characteristics of non-stationary time series data. The proposed method is compared with some well-known time series forecasting techniques from both statistical and computational intelligence methods using demand data of a furniture company. These methods include autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), artificial neural network (ANN), K-nearest neighbors (KNN), recurrent neural network (RNN), support vector machines (SVM) and single layer LSTM. The experimental results indicate that the proposed method is superior among the tested methods in terms of performance measures.

Subfields: Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6b74918de044e2d1b53ca734cb741eb31fe85af6

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In Management Science and Operations Research

In Signal Processing

Citations in the corpus

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022, methodology): “However, auto-regression based approaches are rarely used for anomaly detection in high-order multivariate time series due to their inability to efficiently capture volatile time-series [1].”