Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study

• URL: http://arxiv.org/abs/2203.09170v1
• Date: Thu, 17 Mar 2022 08:47:49 GMT
• Title: Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
• Authors: Grzegorz Dudek, Slawek Smyl, Pawe{\l} Pe{\l}ka
• Abstract summary: We compare recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The proposed RNN produces both point forecasts and predictive intervals (PIs) for them. An empirical study concerning short-term electrical load forecasting for 35 European countries confirmed that the new gated cells with dilation and attention performed best.
• Score: 1.1602089225841632
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The cells we compare include classical long short term memory (LSTM), gated recurrent unit (GRU), modified LSTM with dilation, and two new cells we proposed recently, which are equipped with dilation and attention mechanisms. To model the temporal dependencies of different scales, our RNN architecture has multiple dilated recurrent layers stacked with hierarchical dilations. The proposed RNN produces both point forecasts and predictive intervals (PIs) for them. An empirical study concerning short-term electrical load forecasting for 35 European countries confirmed that the new gated cells with dilation and attention performed best.

Related papers

• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• A predictive physics-aware hybrid reduced order model for reacting flows [65.73506571113623]
  A new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients.
  arXiv  Detail & Related papers  (2023-01-24T08:39:20Z)
• Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting [1.1602089225841632]
  The proposed model is composed of two simultaneously trained tracks: the context track and the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive recurrent cells. The model produces both point forecasts and predictive intervals.
  arXiv  Detail & Related papers  (2022-12-18T07:42:48Z)
• Gated Recurrent Neural Networks with Weighted Time-Delay Feedback [59.125047512495456]
  We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism. We show that $tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures.
  arXiv  Detail & Related papers  (2022-12-01T02:26:34Z)
• Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data [4.2443814047515716]
  We propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time series decomposition within a neural cell. The results show that long- and short-term predictive performance was improved by more than 10%.
  arXiv  Detail & Related papers  (2022-11-30T00:47:03Z)
• An Improved Time Feedforward Connections Recurrent Neural Networks [3.0965505512285967]
  Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. Traditional RNNs models amplify the gradient issue due to the strict time serial dependency. An improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A novel cell structure named Single Gate Recurrent Unit (SGRU) was presented to reduce the number of parameters for RNNs cell.
  arXiv  Detail & Related papers  (2022-11-03T09:32:39Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• ES-dRNN with Dynamic Attention for Short-Term Load Forecasting [1.1602089225841632]
  Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting model combining exponential smoothing and dilated recurrent neural network (ES-dRNN) with a mechanism for dynamic attention.
  arXiv  Detail & Related papers  (2022-03-02T08:39:33Z)
• ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting [1.4502611532302039]
  Short-term load forecasting (STLF) is challenging due to complex time series (TS) This paper proposes a novel hybrid hierarchical deep learning model that deals with multiple seasonality. It combines exponential smoothing (ES) and a recurrent neural network (RNN)
  arXiv  Detail & Related papers  (2021-12-05T19:38:42Z)
• HiPPO: Recurrent Memory with Optimal Polynomial Projections [93.3537706398653]
  We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto bases. Given a measure that specifies the importance of each time step in the past, HiPPO produces an optimal solution to a natural online function approximation problem. This formal framework yields a new memory update mechanism (HiPPO-LegS) that scales through time to remember all history, avoiding priors on the timescale.
  arXiv  Detail & Related papers  (2020-08-17T23:39:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.