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Found 11 papers, 7 papers with code
Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand
We show that our general approach can be seamlessly applied to two distinct neural architectures leading to the state-of-the-art distributional forecasting results in the context of short-term electricity demand forecasting task.
Local and Global Trend Bayesian Exponential Smoothing Models
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models, to model series that grow faster than linear but slower than exponential.
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality.
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance.
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS.
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.
Orbit: Probabilistic Forecast with Exponential Smoothing
Time series forecasting is an active research topic in academia as well as industry.
Computation Methodology
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting.
A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecasting
This paper presents the winning submission of the M4 forecasting competition.
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.
