Deep learning vs classical models for time series forecasting

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Deep Learning vs Classical Models for Time Series Forecasting

Deep Learning Models: Strengths and Capabilities

Deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Bidirectional LSTM (BiLSTM), and hybrid architectures, have shown strong performance in time series forecasting tasks. These models excel at automatically learning complex temporal dependencies and hidden patterns in data, which is often challenging for classical models to capture, especially in multivariate and non-stationary time series scenarios Lara-Benítez2020Mahmoud2020Chandra2021+3 MORE. LSTM and CNN models, in particular, have consistently delivered the most accurate forecasts, with LSTM models often leading in accuracy and CNNs providing efficient and stable results across different parameter settings Lara-Benítez2020Chandra2021Ahmed2022.

Hybrid deep learning models, which combine the strengths of different architectures (e.g., TCN-BiLSTM), further enhance predictive performance by capturing both spatial and temporal dynamics. These models have achieved high accuracy and generalization in real-world datasets, outperforming traditional approaches in handling complex variable interdependencies .

Classical Models: Limitations and Use Cases

Classical statistical models, such as ARIMA and other traditional machine learning methods, have been widely used for time series forecasting. However, they often struggle with high-dimensional, non-linear, and non-stationary data, and may require extensive feature engineering and data preprocessing (e.g., scaling, stationarization) Mahmoud2020Kong2025Song2024. While these models can be effective for simpler, univariate, or stationary time series, their performance tends to lag behind deep learning models as the complexity of the forecasting task increases Lara-Benítez2020Mahmoud2020Song2024.

Comparative Performance and Application Domains

Experimental studies and comprehensive reviews consistently show that deep learning models outperform classical models in terms of accuracy, especially for large-scale, multivariate, and long-sequence forecasting problems Lara-Benítez2020Chandra2021Mahmoud2024+1 MORE. Deep learning models are particularly advantageous in domains such as finance, energy, healthcare, traffic, and meteorology, where capturing intricate temporal patterns and relationships is crucial Ahmed2022Kong2025Sezer2019.

However, deep learning models require more computational resources, expertise in model selection and tuning, and large amounts of data for effective training. In situations with limited data or where interpretability is a priority, classical models or hybrid approaches may still be preferred Miller2024Ahmed2022Song2024.

Challenges and Future Directions

Despite their advantages, deep learning models face challenges such as the need for large datasets, model interpretability, and efficient training for long-term forecasting. Recent advances, including foundation models and the integration of domain knowledge, are being explored to address these issues and further improve forecasting performance Woo2022Miller2024Kong2025+1 MORE.

Conclusion

Deep learning models have established themselves as the leading approach for complex time series forecasting tasks, consistently outperforming classical models in accuracy and flexibility. While classical models remain useful for simpler or smaller-scale problems, the ability of deep learning architectures to learn from large, multivariate, and non-stationary data makes them the preferred choice for modern forecasting challenges. Ongoing research continues to address their limitations, promising even broader applicability and improved performance in the future.

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Most relevant research papers on this topic

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

Long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives for time series forecasting, with LSTMs providing the most accurate forecasts and CNNs achieving comparable performance with less variability and being more efficient.

Literature ReviewHighly Cited

2020·

435citations

·Pedro Lara-Benítez et al.

International journal of neural systems·

DOI

A Survey on Deep Learning for Time-Series Forecasting

Deep learning techniques can effectively predict complex time series problems, offering powerful tools for large data sets and multiple variables with complex relationships.

Highly Cited

2020·

78citations

·A. Mahmoud et al.

Studies in Big Data·

DOI

Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction

Bidirectional and encoder-decoder LSTM networks provide the best accuracy in multi-step ahead time series prediction compared to other deep learning models.

Highly Cited

2021·

216citations

·Rohitash Chandra et al.

IEEE Access·

DOI

Learning Deep Time-index Models for Time Series Forecasting

DeepTime, a meta-optimization framework, effectively learns deep time-index models for time series forecasting, achieving competitive results and efficiency compared to state-of-the-art methods.

2022·

38citations

·Gerald Woo et al.

A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Deep learning has shown significant advantages in time series forecasting, but challenges remain in pandemic prediction, particularly insufficient training data and model interpretability.

Preprint

2024·

68citations

·John A. Miller et al.

ArXiv·

DOI

A Review on Deep Sequential Models for Forecasting Time Series Data

The LSTM model, particularly in the form of a hybrid, is widely used for forecasting time series data, providing the most accurate predictions when used in various applications.

Literature ReviewHighly Cited

2022·

90citations

·Dozdar Mahdi Ahmed et al.

Appl. Comput. Intell. Soft Comput.·

DOI

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting

Hybrid deep learning models, such as the TCN-BiLSTM model, can improve time series forecasting accuracy and generalization by effectively extracting spatial features and temporal dynamics from multivariate data.

2024·

64citations

·Amal Mahmoud et al.

Neural Processing Letters·

DOI

Deep learning for time series forecasting: a survey

Deep Time Series Forecasting (DTSF) models have improved accuracy in various fields, but challenges and future research directions remain.

Literature ReviewHighly Cited

2025·

77citations

·Xiangjie Kong et al.

International Journal of Machine Learning and Cybernetics·

DOI

Deep learning-based time series forecasting

Deep learning-based forecasting models have advanced significantly in capturing correlations and enhancing long-term time series forecasting efficiency across diverse domains.

2024·

58citations

·Xiaobao Song et al.

Artificial Intelligence Review·

DOI

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Deep Learning models significantly outperform traditional Machine Learning models in financial time series forecasting, with potential setbacks and opportunities for future research.

Literature ReviewHighly CitedPreprint

2019·

1335citations

·O. Sezer et al.

ArXiv·

DOI

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