[HTML][HTML] Explainability and interpretability in electric load forecasting using machine learning techniques–A review
Abstract Electric Load Forecasting (ELF) is the central instrument for planning and
controlling demand response programs, electricity trading, and consumption optimization …
controlling demand response programs, electricity trading, and consumption optimization …
Intrusion detection in cloud computing based on time series anomalies utilizing machine learning
The growth of cloud computing is hindered by concerns about privacy and security. Despite
the widespread use of network intrusion detection systems (NIDS), the issue of false …
the widespread use of network intrusion detection systems (NIDS), the issue of false …
Approximations to magic: Finding unusual medical time series
In this work we introduce the new problem of finding time series discords. Time series
discords are subsequences of longer time series that are maximally different to all the rest of …
discords are subsequences of longer time series that are maximally different to all the rest of …
Online causal feature selection for streaming features
D You, R Li, S Liang, M Sun, X Ou… - … on Neural Networks …, 2021 - ieeexplore.ieee.org
Recently, causal feature selection (CFS) has attracted considerable attention due to its
outstanding interpretability and predictability performance. Such a method primarily includes …
outstanding interpretability and predictability performance. Such a method primarily includes …
[HTML][HTML] Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction
Effective control of blood glucose level (BGL) is the key factor in the management of type 1
diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is …
diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is …
Analysis of multivariate time series predictability based on their features
A Kovantsev, P Gladilin - 2020 International conference on …, 2020 - ieeexplore.ieee.org
In this study we explore the features of time-series that can be used for evaluation of their
predictability. We suggest using features based on Kolmogorov-Sinai entropy, correlation …
predictability. We suggest using features based on Kolmogorov-Sinai entropy, correlation …
Relevance feedback based online learning model for resource bottleneck prediction in cloud servers
Cloud servers are highly prone to resource bottleneck failures. In this work, we propose an
ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck …
ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck …
A Windowed correlation-based feature selection method to Improve Time Series Prediction of Dengue Fever cases
T Ferdousi, LW Cohnstaedt, CM Scoglio - IEEE Access, 2021 - ieeexplore.ieee.org
The performance of data-driven models depends on training samples. For accurately
predicting dengue fever cases, historical incidence data are inadequate in many locations …
predicting dengue fever cases, historical incidence data are inadequate in many locations …
Improving multivariate time series forecasting with random walks with restarts on causality graphs
Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields.
In some cases they allow constructing more precise forecasting models, leveraging the …
In some cases they allow constructing more precise forecasting models, leveraging the …
Effectiveness of causality-based predictor selection for statistical downscaling: a case study of rainfall in an Ecuadorian Andes basin
Downscaling aims to take large-scale information and map it to smaller scales to reproduce
local climate signals. An essential step in implementing a parsimonious downscaling model …
local climate signals. An essential step in implementing a parsimonious downscaling model …