Technology-enabled financing of sustainable infrastructure: A case for blockchains and decentralized oracle networks
KHY Chung, D Li, P Adriaens - Technological Forecasting and Social …, 2023 - Elsevier
The capital required to maintain infrastructure in good repair falls short globally. This is
commonly referred to as the “infrastructure finance gap”. To address climate change …
commonly referred to as the “infrastructure finance gap”. To address climate change …
A space-embedding strategy for anomaly detection in multivariate time series
Anomaly detection of time series has always been a hot topic in academia and industry.
However, many existing multivariant time series methods suffer from common challenges …
However, many existing multivariant time series methods suffer from common challenges …
A novel deep learning approach for anomaly detection of time series data
Z Ji, J Gong, J Feng - Scientific Programming, 2021 - Wiley Online Library
Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …
occurrence of anomalies in time series may indicate that some faults or disease will occur …
Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market
The reduction of electricity loss and the effective management of electricity demand are vital
operations for production and distribution electricity enterprises. To achieve these goals …
operations for production and distribution electricity enterprises. To achieve these goals …
Autonomous anomaly detection for streaming data
Anomaly detection from data streams is a hotly studied topic in the machine learning
domain. It is widely considered a challenging task because the underlying patterns exhibited …
domain. It is widely considered a challenging task because the underlying patterns exhibited …
Implementing machine learning to optimize the cost-benefit of urban water clarifier geometrics
H Li, J Sansalone - Water Research, 2022 - Elsevier
Clarification basins are ubiquitous water treatment units applied across urban water
systems. Diverse applications include stormwater systems, stabilization lagoons …
systems. Diverse applications include stormwater systems, stabilization lagoons …
Pidforest: anomaly detection via partial identification
We consider the problem of detecting anomalies in a large dataset. We propose a
framework called Partial Identification which captures the intuition that anomalies are easy to …
framework called Partial Identification which captures the intuition that anomalies are easy to …
Online anomaly detection leveraging stream-based clustering and real-time telemetry
A Putina, D Rossi - IEEE Transactions on Network and Service …, 2020 - ieeexplore.ieee.org
Recent technology evolution allows network equipment to continuously stream a wealth of
“telemetry” information, which pertains to multiple protocols and layers of the stack, at a very …
“telemetry” information, which pertains to multiple protocols and layers of the stack, at a very …
Practical and white-box anomaly detection through unsupervised and active learning
To ensure quality of service and user experience, large Internet companies often monitor
various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies …
various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies …
Reduced robust random cut forest for out-of-distribution detection in machine learning models
H Vardhan, J Sztipanovits - arXiv preprint arXiv:2206.09247, 2022 - arxiv.org
Most machine learning-based regressors extract information from data collected via past
observations of limited length to make predictions in the future. Consequently, when input to …
observations of limited length to make predictions in the future. Consequently, when input to …