# Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality

arXiv: https://arxiv.org/abs/2107.01705 · HTML: https://mlsanity.com/paper/2107.01705/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns](https://mlsanity.com/paper/1909.04293/) (2019)
- [A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes](https://mlsanity.com/paper/1710.04874/) (2017)
- [A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting](https://mlsanity.com/paper/2004.00508/) (2020)
- [Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It](https://mlsanity.com/paper/1908.05864/) (2019)
- [Data-Driven Randomized Learning of Feedforward Neural Networks](https://mlsanity.com/paper/1908.03891/) (2019)

## Built on by (most cited first, 4 of 4)

- [ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting](https://mlsanity.com/paper/2112.02663/) (2021)
- [ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2203.00937/) (2022)
- [Ensembles of Randomized NNs for Pattern-based Time Series Forecasting](https://mlsanity.com/paper/2107.04091/) (2021)
- [Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting](https://mlsanity.com/paper/2203.00980/) (2022)

## Abstract (arXiv)

This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method, by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach useful for forecasting time series with multiple seasonality. In the simulation study, we evaluate the performance of the proposed models and find that they can compete in terms of forecasting accuracy with fully-trained networks. Extremely fast and easy training, simple architecture, ease of implementation, high accuracy as well as dealing with nonstationarity and multiple seasonality in time series make the proposed model very attractive for a wide range of complex time series forecasting problems.