Corrector LSTM: built-in training data correction for improved time-series forecasting

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Baghoussi, Y (Author) Other; Carlos Soares (Author) FEUP; João Mendes-Moreira (Author) FEUP

Journal

Title: Neural Computing and Applications

Vol. 36

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-010-J2C

DOI: 10.1007/s00521-024-09962-x

Abstract (EN): Traditional recurrent neural networks (RNNs) are essential for processing time-series data. However, they function as read-only models, lacking the ability to directly modify the data they learn from. In this study, we introduce the corrector long short-term memory (cLSTM), a Read & Write LSTM architecture that not only learns from the data but also dynamically adjusts it when necessary. The cLSTM model leverages two key components: (a) predicting LSTM¿s cell states using Seasonal Autoregressive Integrated Moving Average (SARIMA) and (b) refining the training data based on discrepancies between actual and forecasted cell states. Our empirical validation demonstrates that cLSTM surpasses read-only LSTM models in forecasting accuracy across the Numenta Anomaly Benchmark (NAB) and M4 Competition datasets. Additionally, cLSTM exhibits superior performance in anomaly detection compared to hierarchical temporal memory (HTM) models. © The Author(s) 2024.

Language: English

Type (Professor's evaluation): Scientific