Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology

Bandara, Kasun; Shi, Peibei; Bergmeir, Christoph; Hewamalage, Hansika; Tran, Quoc; Seaman, Brian

doi:10.1007/978-3-030-36718-3_39

Cited by 123 publications

(91 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(C) Multivariate Regression in AI: The key point in time series study [40] is forecasting. Time series analysis for business prediction helps to forecast the probable future values of a practical field in the industry [41][42][43][44]. The method is also applicable in the health domain to predict the health condition of a person on the last diagnosis data [45].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

et al. 2020

View full text Add to dashboard Cite

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

show abstract

Section: Related Workmentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The key point in time series study [35] is forecasting. Time Series analysis for business prediction helps to forecast the probable future values of a practical field in the industry [36][37][38][39]. The method is also applicable in health to predict the health condition of a person on the last diagnosis data [40].…”

Section: (C) Multivariate Regression In Aimentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal¹,

Sekh²,

Kar³

et al. 2020

Preprint

View full text Add to dashboard Cite

The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

show abstract

“…Tsai et al, leveraged the long short-term neural network (LSTM) algorithm to predict PM2.5 concentrations [17]. Bandara et al, forecasted sales demand with LSTM [18]. Cho et al, proposed a new neural network model called the RNN encoder-decoder model [19].…”

Section: Introductionmentioning

confidence: 99%

Future-Aware Trend Alignment for Sales Predictions

2020

View full text Add to dashboard Cite

Accurately forecasting sales is a significant challenge faced by almost all companies. In particular, most products have short lifecycles without the accumulation of historical sales data. Existing methods either fail to capture the context-specific, irregular trends or to integrate as much information as is available in the face of a data scarcity problem. To address these challenges, we propose a new model, called F-TADA, i.e., future-aware TADA, which is derived from trend alignment with dual-attention multi-task recurrent neural networks (TADA). We utilize two real-world supply chain sales data sets to verify our algorithm’s performance and effectiveness on both long and short lifecycles. The experimental results show that the accuracy of the F-TADA is better than the original model. Our model’s performance could be further improved, however, by appropriately increasing the length of the windows in the decoding stage. Finally, we develop a sales data prediction and analysis decision-making system, which can offer intelligent sales guidance to enterprises.

show abstract

Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology

Cited by 123 publications

References 17 publications

Neural Network Based Country Wise Risk Prediction of COVID-19

Neural Network Based Country Wise Risk Prediction of COVID-19

Neural Network Based Country Wise Risk Prediction of COVID-19

Future-Aware Trend Alignment for Sales Predictions

Contact Info

Product

Resources

About