Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.
Financial time series are sequences of values that change over time, such as stock prices or their volatility (which reflects how much these prices fluctuate). Such data is difficult to predict, being influenced by many factors, such as news, investor behaviour, technological changes, and random events. These influences often overlap, making patterns in the data unstable. As a result, forecasting models either require extensive customisation for specific cases or produce results that are not applicable in practice.
Researchers at the HSE FCS Laboratory for Models and Methods of Computational Pragmatics propose using wavelet transformations to improve the accuracy of financial time series forecasting. Wavelet transformations represent a time series as a sum of components at different levels of detail, allowing noise at various scales to be filtered out.
To validate their approach, the researchers used real-world data on 89 financial assets, including company stocks and cryptocurrencies. The dataset comprised time series of average daily prices and realised volatility calculated from five-minute interval data. The assets were then grouped into clusters, after which the leading assets in each cluster were selected. The authors used this data to compare different forecasting approaches, including classical econometric models, machine learning methods, neural networks, and their own algorithm, the Triple Correction Method. In total, they tested 200,000 model configurations.
The authors’ novel algorithm showed strong performance. Unlike classical models, it does not rely on predefined parameters; instead, it updates them at each forecasting step while simultaneously accounting for multiple types of fluctuations in the data. As a result, the method adapts more effectively to market changes. It ranked second in performance for average daily prices, slightly trailing the naïve forecast under the overall Copeland ranked-choice method, while outperforming other methods in terms of absolute values. For the volatility series, the results were less clear-cut; however, when combined with wavelet transformations, the method often produced forecasts that were the best or close to the best. At the same time, it remained computationally simpler than many alternatives and did not require complex parameter tuning.
Vyacheslav Manevich
'Although the Triple Correction Method does not always produce the best result for each individual series, it consistently delivers strong forecasts in most cases—something that is often lacking in practice. Highly specialised models may perform better, but they tend to lose their effectiveness quickly when conditions change,' comments co-author of the study Vyacheslav Manevich, Research Fellow at the HSE Laboratory for Models and Methods of Computational Pragmatics.
Experiments show that wavelet transformations improve predictions in more than 65% of cases. Unlike, for example, the Fourier transform, they allow both the time and frequency of a signal to be considered simultaneously. As a result, the model is provided with more refined data and can capture patterns more accurately. At the same time, the effect depends on the type of data: for stock prices, the transformations help identify trends against the background of market noise, while for volatility they better capture sharp, irregular changes, which tend to make forecasting particularly challenging.
The authors emphasise that even small improvements in accuracy achieved with such methods can lead to significant increases in profit, especially in high-turnover settings. In the future, the researchers plan to investigate how to automatically select optimal wavelet transformations and how to extend the method to multi-step forecasting, for example in business, energy, or medical applications, where it is important to predict not only the next step but also longer-term changes.
The study was conducted with support from HSE University's Basic Research Programme.
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