Deep Bi-Directional LSTM Networks for Device Workload Forecasting

• Autorzy: Ruta Dymitr , Cen Ling , Vu Quang Hieu

Identyfikatory

DOI

10.15439/2020F213

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Deep convolutional neural networks revolutionized the area of automated objects detection from images. Can the same be achieved in the domain of time series forecasting? Can one build a universal deep network that once trained on the past would be able to deliver accurate predictions reaching deep into the future for any even most diverse time series? This work is a first step in an attempt to address such a challenge in the context of a FEDCSIS'2020 Competition dedicated to network device workload prediction based on their historical time series data. We have developed and pre-trained a universal 3-layer bi-directional Long-Short-Term-Memory (LSTM) regression network that reported the most accurate hourly predictions of the weekly workload time series from the thousands of different network devices with diverse shape and seasonality profiles. We will also show how intuitive human-led post-processing of the raw LSTM predictions could easily destroy the generalization abilities of such prediction model.

Słowa kluczowe

EN

workload prediction; time series; long short-term memory; LSTM; ensemble averaging

PL

przewidywanie obciążenia; szereg czasowy; pamięć krótkotrwała; LSTM; uśrednianie zespołowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 115--118
• Opis fizyczny: Bibliogr. 13 poz., wz., wykr.

Twórcy

autor

Ruta Dymitr

• EBTIC, Khalifa University, UAE

autor

Cen Ling

• EBTIC, Khalifa University, UAE

autor

Vu Quang Hieu

• Zalora, Singapore

Bibliografia

• 1. FedCSIS 2020 Challenge: Network Device Workload Prediction, https://knowledgepit.ml/fedcsis20-challenge/.
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• 11. Y. Zhu, W. Zhang, Y. Chen and H. Gao, "A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment," EURASIP Journal on Wireless Communications and Networking, Article number: 274, 2019.
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• 13. S. Hochreiter, J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).