Reference: Bryan Lim, Stefan Zohren and Stephen Roberts. Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020
Paper link: https://arxiv.org/abs/1901.08096
Despite the recent popularity of deep generative state space models, few comparisons have been made between network architectures and the inference steps of the Bayesian filtering framework -- with most models simultaneously approximating both state transition and update steps with a single recurrent neural network (RNN). In this paper, we introduce the Recurrent Neural Filter (RNF), a novel recurrent autoencoder architecture that learns distinct representations for each Bayesian filtering step, captured by a series of encoders and decoders. Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, and also facilitate multistep prediction through the separation of encoder stages.
This code repository contains the demo code for the Standard RNF.
To download default UCI electricity dataset, and train using the default RNF hyperparameters, run:
bash run.shPlease refer to run.sh for additional usage instructions.
The key scripts are divided into:
- script_download_data.py: Downloads the default UCI power dataset and formats the data
- script_hyperparam_opt.py: Runs the full hyperparameter optimisation for the RNF
- script_train_fixed_params.py: Trains the RNF using pre-defined hyperparameters.
- script_forecasting: Performs the one-step-ahead and multistep forecasting evaluations.