A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Thiyagarajan, Karthick; Kodagoda, Sarath; Ulapane, Nalika; prasad, mukesh

doi:10.36227/techrxiv.12145371.v1

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Their algorithm, Prophet [39], is based on an additive model where nonlinear trends are fit with seasonality and holidays or other recurrent events. It was applied in numerous studies and achieved state-of-theart (SOTA) performance [40,44]. Recently, NeuralProphet [39,41], a neural network-based extension of Prophet, reached SOTA results for several time series prediction tasks.…”

Section: Related Workmentioning

confidence: 99%

Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly

Kalifa

Singer

Guy

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Consumer demand forecasting is of high importance for many e-commerce applications, including supply chain optimization, advertisement placement, and delivery speed optimization. However, reliable time series sales forecasting for e-commerce is difficult, especially during periods with many anomalies, as can often happen during pandemics, abnormal weather, or sports events. Although many time series algorithms have been applied to the task, prediction during anomalies still remains a challenge. In this work, we hypothesize that leveraging external knowledge found in world events can help overcome the challenge of prediction under anomalies. We mine a large repository of 40 years of world events and their textual representations. Further, we present a novel methodology based on transformers to construct an embedding of a day based on the relations of the day's events. Those embeddings are then used to forecast future consumer behavior. We empirically evaluate the methods over a large e-commerce products sales dataset, extracted from eBay, one of the world's largest online marketplaces. We show over numerous categories that our method outperforms state-of-the-art baselines during anomalies. CCS CONCEPTS• Computing methodologies → Machine learning.

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Section: Related Workmentioning

confidence: 99%

Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly

Kalifa

Singer

Guy

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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“…The temporal forecasting performance of different models such as Facebook's Prophet method, TBATS model, ARIMA model, ETS model, and the Bagged model were evaluated for forecasting sewer air temperature sensor data. The study outcomes indicated that the performance of Facebook's Prophet method was better in terms of accuracy than other forecasting models for forecasting a week period and a daily period [19]. In this paper, we use the surface temperature sensor data to evaluate the forecasting performances of the seasonal ARIMA model combined with Hyndman and Khandakar algorithm, Facebook Prophet method, ETS model, and Bagged model.…”

Section: Brief Review On Related Workmentioning

confidence: 99%

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Thiyagarajan¹,

Kodagoda²,

Ulapane³

2020

Preprint

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Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

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“…Corrosion is a significant cause of structural degradation in those pipelines. Water utilities assess corrosion conditions using a variety of sensing technologies [1]- [7] in order to avert catastrophic pipe failures. To extend the useful service life of the pipeline and reduce costs associated with pipe replacement, severely corroded pipes are replaced, while moderately corroded pipes are applied with protective linings [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Battery-free UHF-RFID Sensor Wireless Signals for In-pipe Robotic Applications

2021

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A reliable robotic localization method is required for comparing three-dimensional pipe maps obtained via laser scans at various times for accurately monitoring the evolution of internal pipe surface defects. Existing robotic localization methods have limitations when visual features vanish due to changes in the pipe environment or when encoder data becomes highly uncertain due to long-distance robotic traverses. To address this issue, we leverage battery-free ultra-high frequency radio frequency identification (UHF-RFID) sensors for transmitting wireless signals to a two-antenna reader integrated mobile robotic system. Although there are literature on the investigation of UHF-RFID behaviors and their applications in indoor environments, analysis of the same for in-pipe scenarios was not well studied. In this paper, we evaluate the UHF-RFID sensor signals inside a field extracted pipeline. Firstly, we examine the UHF-RFID sensor signal patterns through repeated robotic scans. Secondly, we examine how the placement of UHF-RFID reader antennas affects the transmission of UHF-RFID sensor signals, as well as we study the effects of robotic traverse direction and speed on the UHF-RFID wireless signals. Finally, we examine whether identical UHF-RFID sensors generate the same pattern when placed in a pipeline. Overall, the experimental evaluation demonstrates that the use of two-antenna UHF-RFID readers can ameliorate the capabilities of robotic localization in the pipeline.

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A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Cited by 3 publications

References 26 publications

Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly

Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly

Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

Evaluation of Battery-free UHF-RFID Sensor Wireless Signals for In-pipe Robotic Applications

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