Time Series vs. Machine Learning: A Systematic Evaluation

Forecasting is important in finance, as it helps investors, analysts, and institutions make informed decisions under uncertainty. Up to now, most forecasting techniques have relied on traditional time series methods, such as ARIMA, GARCH, and exponential smoothing. However, with recent advancements in machine learning and artificial intelligence, these technologies have increasingly found applications in financial forecasting. Their ability to capture complex, nonlinear relationships and process large volumes of data has opened new possibilities for improving prediction accuracy in areas such as asset prices, volatility, and risk.

Reference [1] presents a systematic comparison of traditional time series techniques with newer AI/ML approaches. It highlights the weaknesses of traditional time series methods, notably they assume stationarity and linear relationships, which often do not hold in financial markets. These models struggle with non-stationary data, non-linear dynamics, and large datasets, limiting their ability to capture the full complexity of market behavior.

The paper also discusses the advantages of AI-driven methods, particularly that they excel at capturing complex, non-linear relationships in financial data, and adapting to changing market conditions without manual intervention. They also handle large, high-dimensional datasets effectively, uncovering hidden patterns and making more accurate predictions than traditional models.

The authors made several comparisons using criteria such as,

• Accuracy
• Computational Complexity
• Flexibility and Adaptability
• Interpretability

The authors pointed out,

The comparison of both approaches revealed that while traditional methods are more interpretable and computationally efficient, AI-driven techniques provide greater accuracy and adaptability, especially when dealing with the dynamic and volatile nature of modern financial markets. However, the challenge of obtaining high-quality, reliable data and avoiding overfitting remains for both types of models.

In practice, the decision to use traditional methods versus AI-driven approaches depends largely on the nature of the financial data and the specific forecasting needs. Traditional methods may still be the preferred choice for simpler, well-behaved datasets where linearity and stationarity are present, or when computational resources are limited. They are also suitable for scenarios where interpretability is essential, such as regulatory environments or when model transparency is required. Conversely, AI-driven models should be considered when forecasting complex, non-linear, or high-dimensional financial data, such as stock prices or forex rates, where traditional models struggle. These models are particularly useful when predictive accuracy is paramount, and sufficient computational resources are available to handle the increased complexity.

In short, the new AI/ML techniques offer advantages but also come with disadvantages. However, nothing prevents us from combining these two approaches and leveraging their respective strengths.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Gwokkwan Sun, and Shuhan Deng, Financial Time Series Forecasting: A Comparison Between Traditional Methods and AI-Driven Techniques, Journal of Computer, Signal, and System Research, Vol. 2 No. 2 (2025)

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