
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time...
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Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely ResourceConstrained Devices
Significant efforts are being invested to bring stateoftheart classif...
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Versatile and Robust Transient Stability Assessment via Instance Transfer Learning
To support N1 prefault transient stability assessment, this paper intr...
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SQAPlanner: Generating DataInformed Software Quality Improvement Plans
Software Quality Assurance (SQA) planning aims to define proactive plans...
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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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Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting ...
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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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Model selection in reconciling hierarchical time series
Model selection has been proven an effective strategy for improving accu...
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A Strong Baseline for Weekly Time Series Forecasting
Many businesses and industries require accurate forecasts for weekly tim...
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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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Seasonal Averaged OneDependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in HighDimensional Stream Classification
Stream classification methods classify a continuous stream of data as ne...
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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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Time Series Regression
This paper introduces Time Series Regression (TSR): a littlestudied tas...
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Monash University, UEA, UCR Time Series Regression Archive
Time series research has gathered lots of interests in the last decade, ...
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Towards Accurate Predictions and Causal 'Whatif' Analyses for Planning and Policymaking: A Case Study in Emergency Medical Services Demand
Emergency Medical Services (EMS) demand load has become a considerable b...
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Machine learning applications in time series hierarchical forecasting
Hierarchical forecasting (HF) is needed in many situations in the supply...
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LSTMMSNet: 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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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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LoRMIkA: Local Rulebased Model Interpretability with koptimal Associations
As we rely more and more on machine learning models for reallife decisi...
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Sales Demand Forecast in Ecommerce using a Long ShortTerm Memory Neural Network Methodology
Generating accurate and reliable sales forecasts is crucial in the Ecom...
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Forecasting Across Time Series Databases using Long ShortTerm Memory Networks on Groups of Similar Series
With the advent of Big Data, nowadays in many applications databases con...
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Christoph Bergmeir
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