DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe

2025 · doi:10.22541/au.176376260.03485604/v1
preprint OA: closed
Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text ⓘ

The paper proposes a bio-inspired, “DNA”-inspired encoding framework that converts observable micro-patterns in a financial time series into symbolic “Financial DNA” sequences, which are then used with a probabilistic state-transition mechanism to forecast the direction of future movements. The authors evaluate the method on Bitcoin hourly OHLCV data using a rolling backtest across several forecast horizons, finding the best performance for predicting 4-hour-ahead direction with a win ratio of 0.729. The work is explicitly labeled as a preprint and notes that the results may be preliminary and not peer reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

In this work, we introduce a bio-inspired encoding framework for forecasting the direction of financial time series. Motivated by the limitations of linear models and the opacity of many deep learning approaches, we draw an analogy to genetics: observable micro-patterns are encoded into symbolic "Financial DNA" sequences. These sequences are then analyzed using a probabilistic state-transition mechanism to estimate the likelihood of subsequent market directions. We evaluate the approach on Bitcoin hourly OHLCV data with a rolling backtest. Among the horizons considered, modeling transitions from current Financial DNA patterns to the 4-hour-ahead price direction yields the strongest results, achieving a win ratio of 0.729. The findings suggest that compact, interpretable symbolic representations can capture salient, recurring structures in noisy, non-stationary markets and support effective directional forecasts.
Full text 1,941 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

In this work, we introduce a bio-inspired encoding framework for forecasting the direction of financial time series. Motivated by the limitations of linear models and the opacity of many deep learning approaches, we draw an analogy to genetics: observable micro-patterns are encoded into symbolic "Financial DNA" sequences. These sequences are then analyzed using a probabilistic state-transition mechanism to estimate the likelihood of subsequent market directions. We evaluate the approach on Bitcoin hourly OHLCV data with a rolling backtest. Among the horizons considered, modeling transitions from current Financial DNA patterns to the 4-hour-ahead price direction yields the strongest results, achieving a win ratio of 0.729. The findings suggest that compact, interpretable symbolic representations can capture salient, recurring structures in noisy, non-stationary markets and support effective directional forecasts. Supplementary Material File (a glimpse into the next 4-hour timeframe.pdf) - Download - 299.81 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Keywords

Authors Metrics & Citations Metrics Article Usage 175views 76downloads Citations Download citation Bao Bui-Quang. DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe. Authorea. 21 November 2025. DOI: https://doi.org/10.22541/au.176376260.03485604/v1 DOI: https://doi.org/10.22541/au.176376260.03485604/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00