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Found 11 papers, 6 papers with code
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach
We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets.
On Forecast Stability
To fill this gap, we propose a simple linear-interpolation-based approach that is applicable to stabilise the forecasts provided by any base model vertically and horizontally.
Handling Concept Drift in Global Time Series Forecasting
Handling concept drift in forecasting is essential for many ML methods in use nowadays, however, the prior work only proposes methods to handle concept drift in the classification domain.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
On the other hand, in the forecasting community, general-purpose tree-based regression algorithms (forests, gradient-boosting) have become popular recently due to their ease of use and accuracy.
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area.
Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly outperforming traditional univariate forecasting models that work on isolated series.
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting
Many businesses and industries require accurate forecasts for weekly time series nowadays.
Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification
This paper explores how to best handle seasonal drift in the specific context of news article categorization (or classification/tagging), where seasonal drift is overwhelmingly the main type of drift present in the data, and for which the data are high-dimensional.
Simulation and Optimisation of Air Conditioning Systems using Machine Learning
In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy.
