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08/25/2023
Deep Active Audio Feature Learning in Resource-Constrained Environments
The scarcity of labelled data makes training Deep Neural Network (DNN) m...
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04/04/2023
Handling Concept Drift in Global Time Series Forecasting
Machine learning (ML) based time series forecasting models often require...
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11/16/2022
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Threshold Autoregressive (TAR) models have been widely used by statistic...
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11/14/2022
Dealing with missing data using attention and latent space regularization
Most practical data science problems encounter missing data. A wide vari...
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09/19/2022
Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
The electricity industry is heavily implementing smart grid technologies...
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09/15/2022
FRANS: Automatic Feature Extraction for Time Series Forecasting
Feature extraction methods help in dimensionality reduction and capture ...
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03/21/2022
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
Machine Learning (ML) and Deep Learning (DL) methods are increasingly re...
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02/15/2022
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
Accurate electricity demand forecasts play a crucial role in sustainable...
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11/29/2021
NeuralProphet: Explainable Forecasting at Scale
We introduce NeuralProphet, a successor to Facebook Prophet, which set a...
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11/13/2021
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts
Global Forecasting Models (GFM) that are trained across a set of multipl...
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08/13/2021
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices
Deep Learning has celebrated resounding successes in many application ar...
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08/08/2021
A Look at the Evaluation Setup of the M5 Forecasting Competition
Forecast evaluation plays a key role in how empirical evidence shapes th...
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07/28/2021
MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
The decomposition of time series into components is an important task th...
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05/14/2021
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time...
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03/05/2021
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
Significant efforts are being invested to bring state-of-the-art classif...
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02/20/2021
Versatile and Robust Transient Stability Assessment via Instance Transfer Learning
To support N-1 pre-fault transient stability assessment, this paper intr...
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02/19/2021
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans
Software Quality Assurance (SQA) planning aims to define proactive plans...
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01/31/2021
MultiRocket: Effective summary statistics for convolutional outputs in time series classification
Rocket and MiniRocket, while two of the fastest methods for time series ...
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12/30/2020
Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting ...
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12/23/2020
Global Models for Time Series Forecasting: A Simulation Study
In the current context of Big Data, the nature of many forecasting probl...
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10/21/2020
Model selection in reconciling hierarchical time series
Model selection has been proven an effective strategy for improving accu...
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10/16/2020
A Strong Baseline for Weekly Time Series Forecasting
Many businesses and industries require accurate forecasts for weekly tim...
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08/06/2020
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Forecasting models that are trained across sets of many time series, kno...
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06/27/2020
Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification
Stream classification methods classify a continuous stream of data as ne...
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06/27/2020
Simulation and Optimisation of Air Conditioning Systems using Machine Learning
In building management, usually static thermal setpoints are used to mai...
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06/23/2020
Time Series Regression
This paper introduces Time Series Regression (TSR): a little-studied tas...
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06/19/2020
Monash University, UEA, UCR Time Series Regression Archive
Time series research has gathered lots of interests in the last decade, ...
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04/25/2020
Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand
Emergency Medical Services (EMS) demand load has become a considerable b...
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12/01/2019
Machine learning applications in time series hierarchical forecasting
Hierarchical forecasting (HF) is needed in many situations in the supply...
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09/10/2019
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
Generating forecasts for time series with multiple seasonal cycles is an...
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09/02/2019
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Recurrent Neural Networks (RNN) have become competitive forecasting meth...
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08/11/2019
LoRMIkA: Local Rule-based Model Interpretability with k-optimal Associations
As we rely more and more on machine learning models for real-life decisi...
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01/13/2019
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Generating accurate and reliable sales forecasts is crucial in the E-com...
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10/09/2017
