Pith. sign in

REVIEW 1 cited by

Time Series Forecasting Using LSTM Networks: A Symbolic Approach

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2003.05672 v1 pith:E45GG3ZC submitted 2020-03-12 cs.LG stat.ML

Steven Elsworth , Stefan Güttel This is my paper
classification cs.LGstat.ML
keywords seriessymbolictimeforecastingrepresentationadditionaforementionedalleviate
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights. A combination of a recurrent neural network with a dimension-reducing symbolic representation is proposed and applied for the purpose of time series forecasting. It is shown that the symbolic representation can help to alleviate some of the aforementioned problems and, in addition, might allow for faster training without sacrificing the forecast performance.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

    cs.LG 2025-11 conditional novelty 5.0 of 10

    CaReTS forecasts multi-step time series by combining a trend classifier with a deviation regressor in a residual, uncertainty-weighted multi-task framework.

Pith tools