An optimized model using LSTM network for demand forecasting

Abbasimehr, Hossein; Shabani, Mostafa; Yousefi, Mohsen

doi:10.1016/j.cie.2020.106435

Cited by 312 publications

(161 citation statements)

References 45 publications

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“…Deep learning methods are already successfully used for predicting time series and they have been shown to outperform classic statistical methods as well as machine learning methods [3,[33][34][35][36]. LSTM Neural Networks represent a further development of Recurrent Neural Networks (RNN), and were used for inventory forecasting by Abbasimehr et al [35]. The results of the study on Neural Networks for demand forecasting intermittent time series by Kourentzes [3] were ambiguous due to different evaluation metrics.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

See 1 more Smart Citation

Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

Kiefer¹,

Grimm²,

Bauer³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Forecasting intermittent and lumpy demand is challenging. Demand occurs only sporadically and, when it does, it can vary considerably. Forecast errors are costly, resulting in obsolescent stock or unmet demand. Methods from statistics, machine learning and deep learning have been used to predict such demand patterns. Traditional accuracy metrics are often employed to evaluate the forecasts, however these come with major drawbacks such as not taking horizontal and vertical shifts over the forecasting horizon into account, or indeed stock-keeping or opportunity costs. This results in a disadvantageous selection of methods in the context of intermittent and lumpy demand forecasts. In our study, we compare methods from statistics, machine learning and deep learning by applying a novel metric called Stock-keeping-oriented Prediction Error Costs (SPEC), which overcomes the drawbacks associated with traditional metrics. Taking the SPEC metric into account, the Croston algorithm achieves the best result, just ahead of a Long Short-Term Memory Neural Network.

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Section: Related Work and Research Gapmentioning

confidence: 99%

“…In this way, in contrast to conventional Recurrent Neural Networks (RNNs), LSTMs enable a kind of memory of past experiences. Abbasimehr et al [35] used LSTMs with great results in demand forecasting time series. In our experiment we use the model from the library tensorflow 2.0.…”

Section: Figure 5 Forecasting On a Rolling Windowmentioning

confidence: 99%

Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

Kiefer¹,

Grimm²,

Bauer³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…16 Ma et al 17 combined the Grid concept and LSTM (G-LSTM) for the forecasting of fuel cell degradation. More than that, the latest researches applied LSTM to more hot areas of prediction, for example, electricity price forecasting, 18 flood forecasting, 19 wind speed forecasting, 20 air pollution forecasting, 21 voltages forecasting, 22 demand forecasting, 23 photovoltaic power forecasting, 24 and so forth. 25,26 The LSTM was wielded to alleviate the vanished gradient in a multi-layer network architecture.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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“…In simple terms, the forget gate determines which information should be abandoned from previous unit cell states, the input gate determines which input information and candidate cell state should be updated, and the output gate outputs information based on the state of the unit cell state. The vector formulas for a LSTM can then be written as: [27,28] = (…”

Section: A Lstm-rnnmentioning

confidence: 99%

Defect Depth Determination in Laser Infrared Thermography Based on LSTM-RNN

et al. 2020

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Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.

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An optimized model using LSTM network for demand forecasting

Cited by 312 publications

References 45 publications

Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Defect Depth Determination in Laser Infrared Thermography Based on LSTM-RNN

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