Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Punia, Sushil; Νικολόπουλος, Κωνσταντίνος; Singh, SP; Madaan, Jitendra; Litsiou, Konstantia

doi:10.1080/00207543.2020.1735666

Cited by 112 publications

(69 citation statements)

References 48 publications

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“…The LSTM were implemented using Keras library in R ( Chollet, 2015 ). The work of Punia, Nikolopoulos et al (2020) was followed for implementation and hyperparameter optimization of the LSTM networks.…”

Section: Description Of Machine- and Deep-learning Forecasting Methodmentioning

confidence: 99%

“…We calculated the Mean Absolute Scaled Error (MASE) and the Symmetric Mean Absolute Percentage Error (SMAPE) for each iteration ( Makridakis et al, 2020 ; Shankar, Ilavarasan, Punia & Singh, 2019 ). We calculated the relative errors by dividing with the corresponding error from the naïve method ( Punia, Nikolopoulos, Singh, Madaan & Litsiou, 2020 ). We report in Table 2 the relative (to naïve) medians for: MASE (RelMdMASE), and SMAPE (RelMdMAPE).…”

Section: Forecasting the Evolution Of The Pandemicmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Νικολόπουλος

Punia

Schäfers

et al. 2021

European Journal of Operational Research

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412

292

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Highlights We provide predictive analytics tools for immediate use during COVID-19. We use data from the UK, USA, India, Germany, Singapore up to mid-April 2020. We forecast COVID-19 growth rates at country-level. We use auxiliary data (Google trends) to model excess demand. We forecast the excess demand for products and services at country-level.

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Section: Description Of Machine- and Deep-learning Forecasting Methodmentioning

confidence: 99%

Section: Forecasting the Evolution Of The Pandemicmentioning

confidence: 99%

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Νικολόπουλος

Punia

Schäfers

et al. 2021

European Journal of Operational Research

Self Cite

412

292

View full text Add to dashboard Cite

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“…The Conv1D-RF based on OFSM had the highest accuracy (94.27%) and K coefficient (0.917) among all types of networks, so this hybrid network is assumed to be the best choice in this study. This study compared the hybrid Conv1D-RF network with popular deep-learning based networks, including LSTM-RF [49], ResNet [50], and U-Net [51]. LSTM-RF uses RF instead of the FC layer to make the final classification decision.…”

Section: Rf-rfe Ofsmmentioning

confidence: 99%

“…This study compared the hybrid Conv1D-RF network with popular deep-learning based networks, including LSTM-RF [49], ResNet [50], and U-Net [51]. LSTM-RF uses RF instead of the FC layer to make the final classification decision.…”

Section: Comparison Of Conv1d-rf With Mainstream Networkmentioning

confidence: 99%

Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Yang

et al. 2020

Remote Sensing

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Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

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“…Wang et al [24] dealt with the problem of long-term dependence in sequence data due to insufficient memory capacity in LSTM cells, solving it using an attention-aware bidirectional multi-residual recurrent neural network (ABMRNN). Punia et al [45] presented a new predictive method that combines LSTM and random forest (RF), the efficiency of which is compared to other methods, such as neural networks, multiple regression, ARIMAX (Autoregressive Integrated Moving Average with Explanatory Variable), etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Cited by 112 publications

References 48 publications

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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