# Sequence Prediction using Spectral RNNs

## TL;DR

This paper introduces Spectral RNNs that integrate Fourier methods with recurrent neural networks to improve efficiency and reduce weights, demonstrating effectiveness on chaotic, power load, and motion capture data.

## Contribution

It presents a novel combination of Fourier transforms with RNNs for efficient sequence prediction, enabling weight reduction and improved processing of multiple samples.

## Key findings

- Effective prediction of chaotic Mackey-Glass data
- Successful application to real-world power load data
- Demonstrated efficiency gains through spectral filtering

## Abstract

Fourier methods have a long and proven track record as an excellent tool in data processing. As memory and computational constraints gain importance in embedded and mobile applications, we propose to combine Fourier methods and recurrent neural network architectures. The short-time Fourier transform allows us to efficiently process multiple samples at a time. Additionally, weight reductions trough low pass filtering is possible. We predict time series data drawn from the chaotic Mackey-Glass differential equation and real-world power load and motion capture data.