Forecasting with Deep Learning

Gissel Velarde
2/17/2023
cs.LGcs.AI

No official code found. Best implementation: Alebuenoaz/LSTM-and-GRU-Time-Series-Forecasting (14 stars, last commit 2022-05-22).

Abstract

This paper presents a method for time series forecasting with deep learning and its assessment on two datasets. The method starts with data preparation, followed by model training and evaluation. The final step is a visual inspection. Experimental work demonstrates that a single time series can be used to train deep learning networks if time series in a dataset contain patterns that repeat even with a certain variation. However, for less structured time series such as stock market closing prices, the networks perform just like a baseline that repeats the last observed value. The implementation of the method as well as the experiments are open-source.

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Code Implementations(1)

Alebuenoaz/LSTM-and-GRU-Time-Series-ForecastingCommunity100%
145Python, Jupyter NotebookMar 5, 20224 years ago

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Cite this paper

@article{velarde2023forecasting,
  title  = {Forecasting with Deep Learning},
  author = {Gissel Velarde},
  year   = {2023},
  eprint = {2302.12027},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.12027v1}
}

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