# Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms

arXiv: https://arxiv.org/abs/2411.17499 · HTML: https://mlsanity.com/paper/2411.17499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 90 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors](https://mlsanity.com/paper/2305.18803/) (2023)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Dilated Recurrent Neural Networks](https://mlsanity.com/paper/1710.02224/) (2017)
- [Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality](https://mlsanity.com/paper/2008.00444/) (2020)
- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://mlsanity.com/paper/2104.05522/) (2021)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Dataset2Vec: Learning Dataset Meta-Features](https://mlsanity.com/paper/1905.11063/) (2019)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing](https://mlsanity.com/paper/2511.18258/) (2025)

## Abstract (arXiv)

TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirteen manufacturing datasets, focusing on their applicability in manufacturing. Each algorithm was selected based on its TSF category to ensure a representative set of algorithms. The evaluation includes different scenarios to evaluate the models using two problem categories and two forecasting horizons. To evaluate the performance, the WAPE was calculated, and additional post hoc analyses were conducted to assess the significance of observed differences. Only algorithms with codes from open-source libraries were utilized, and no hyperparameter tuning was done. This allowed us to evaluate the algorithms as "out-of-the-box" solutions that can be easily implemented, ensuring their usability within the manufacturing by practitioners with limited technical knowledge. This aligns to facilitate the adoption of these techniques in smart manufacturing systems. Based on the results, transformer and MLP-based architectures demonstrated the best performance with MLP-based architecture winning the most scenarios. For univariate TSF, PatchTST emerged as the most robust, particularly for long-term horizons, while for multivariate problems, MLP-based architectures like N-HITS and TiDE showed superior results. The study revealed that simpler algorithms like XGBoost could outperform complex algorithms in certain tasks. These findings challenge the assumption that more sophisticated models produce better results. Additionally, the research highlighted the importance of computational resource considerations, showing variations in runtime and memory usage across different algorithms.