mlsanityData to 7 Oct 2026

Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques

arXiv 2503.20148 ↗First posted 26 Mar 2025cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. This paper briefly describes and compiles suitable algorithms for TS regression task. We compare these algorithms against each other and the classic ARIMA method using diverse datasets: complete data, data with outliers, and data with missing values. The focus is on forecasting accuracy, particularly for long-term predictions. This research aids in selecting the most appropriate algorithm based on forecasting needs and data characteristics.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 67 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

12 of 67 references.

References · 12

  1. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  2. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  3. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  4. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  5. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  6. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  7. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  8. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  9. 2024Large Language Models for Time Series: A Surveycited by 113
  10. 2021Deeptime: a Python library for machine learning dynamical models from time series datacited by 36
  11. 2021Time series compression: a surveycited by 16
  12. 2021Asymmetrical Bi-RNN for pedestrian trajectory encodingcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systemscited by 1