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Found 13 papers, 8 papers with code
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region.
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.
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.
A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study
While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output.
Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations
We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models.
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.
Global Models for Time Series Forecasting: A Simulation Study
Our experiments demonstrate that when trained as global forecasting models, techniques such as RNNs and LGBMs, which have complex non-linear modelling capabilities, are competitive methods in general under challenging forecasting scenarios such as series having short lengths, datasets with heterogeneous series and having minimal prior knowledge of the patterns of the series.
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques.
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 burden for many government authorities, and EMS demand is often an early indicator for stress in communities, a warning sign of emerging problems.
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 important use-case for many industries nowadays.
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition.
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model.
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.
