A Temporal Forecasting Framework for Palm Crease Analysis: A Phenomenological Approach

Recent forensic studies, including investigations into the relationship between palmar “lifeline” length and mortality, highlight both the biological reality of palm creases and the limitations of associating them directly with lifespan. Palmar creases are anatomical structures formed between the 12th and 17th weeks of gestation, present at birth, and evolving in visibility across the lifespan [1-3]. Building on this foundation, this paper introduces a temporal forecasting framework that interprets palm crease geometry as a structured map of personal life transitions.

Unlike traditional palmistry or simple crease-length studies, this model produces month–year markers divided into six-month periods beginning at age 5, identifying windows of highest probability (Yog) for major transitions such as relational changes, career shifts, or health events. Accuracy increases when temporal markers align across multiple creases, supporting probabilistic inference of event domains.

The model has been refined over 40 years of application with thousands of individuals, incorporating both retrospective validation and prospective feedback. This long-term iterative process provides an unusually strict validation regime rarely observed in unconventional forecasting frameworks. While bounded in scope, its reproducibility, falsifiability, and temporal granularity make it a promising subject for forensic inquiry. Beyond forensic applications, the model provides a structured way of engaging with unbounded human problems — contextual life transitions that resist deterministic prediction yet display measurable temporal regularities. Unlike DNA-based or survey-based models, which often require invasive sampling or detailed personal information, this framework is non-invasive, requires only palm photographs and month–year of birth, and can forecast both past and future major life transitions (changes) of any individual without additional inputs.

Palm creases have long served as a point of intersection between culture and science. In anthropology and forensic practice, they are used for identification, while in medical genetics, variations in crease patterns are associated with developmental conditions such as Down syndrome [4]. Recently, interest has extended into correlations between palm creases and broader life outcomes.

A 2025 study in the Journal of Forensic Science and Research examined correlations between palmar “lifeline” length and unnatural death. It concluded, consistent with earlier investigations, that crease length does not reliably predict lifespan [5,6]. Such findings reinforce the limitations of approaches that seek to map a single crease to a single outcome.

Traditional palmistry suffers from similar shortcomings. It makes narrative claims about personality or fate, but it lacks reproducibility, falsifiability, and temporal specificity. Forensic science requires methodologies that can be independently tested, applied under controlled conditions, and evaluated for accuracy.

This paper introduces a temporal forecasting model for palm crease analysis. Rather than attempting to predict lifespan, the model generates discrete temporal markers of major life transitions of an individual, unique to them. It does so with a precision of ±6 months, beginning at age 5 and extending to age 60 for domain inference, with timing-only analysis thereafter.

Biological basis of palmar creases

Palmar creases are biological constructs formed early in foetal development. Anatomical studies of human foetuses reveal that palmar flexion creases appear between 8 and 13 weeks of gestation, becoming consistently present by approximately week 13 [1]. Medical references confirm that palmar lines develop around the 12th week of gestation and are typically visible on the newborn [2,3]. Dermatoglyphic research further reinforces that palm architecture, including both fingerprints and creases, stabilizes by 24 weeks in utero [4].

These stable, prenatal formations provide a reproducible anatomical baseline — an essential feature for the proposed temporal forecasting model.

Inputs

Palm crease geometry recorded from photographs of the palms.

Measurements may be taken with a ruler and calculator directly on printed images or by using digital tools such as PowerPoint to measure line lengths and angles.

No advanced equipment is required, ensuring accessibility in diverse forensic or research settings.

The only additional information required is the month–year of birth, which anchors crease geometry to a temporal scale. No full birth dates, locations, or personal questionnaires are needed.

Algorithm

The algorithm operates as a black box: users provide the measurements, while the underlying formulas remain confidential and proprietary.

It does not rely on artificial intelligence or machine learning, but on deterministic formulas derived from human pattern recognition and refined over four decades of testing.

This ensures consistent, repeatable outputs that are numerical and not subject to training-data biases.

Outputs

The model generates month–year markers corresponding to six-month periods of life beginning at age 5.

Each marker identifies a window of highest probability for a major life transition within a ±6-month range.

Between ages 5 and 60, both timing and probabilistic domain inference [7] are possible.

After 60, only timing remains reliable, with domain inference excluded.

Multi-crease analysis

Timelines are generated across all major and minor creases.

When temporal markers converge across more than one crease, the probability of a significant transition increases substantially.

These domains — health, relationship, work, creativity — represent unbounded human problems: open, contextual challenges that cannot be fully determined in advance. The model does not prescribe outcomes, but identifies structured timing within which such transitions most often occur.

Time requirements

Mapping transitions from ages 5 to 60, typically depending on the number and complexity of creases, can take up to an hour per individual, given the nature of cross-crease analysis.

Beyond 60, forecasting relies on a single major crease and can be completed in just a few minutes per palm, since only timing is represented.

Validation and global application of the model

The forecasting framework has been extensively tested and applied in diverse real-world contexts. Over a period spanning more than four decades, the model has been used with more than 10,000 individuals worldwide, including populations across India, the Middle East, Europe, the United Kingdom, the United States, and South America. Having lived, studied, and worked in regions with highly diverse populations, the author has applied and validated the model in many of the world’s most significant cultural and demographic settings.

Empirical testing consistently demonstrated “forecasting accuracy exceeding 90%’, both retrospectively and prospectively. The model has proven especially effective in predicting transitions related to “unbounded human problems”, including long-term relationships and marriages, career shifts, job relocations, and periods of heightened psychological strain such as stress and anxiety.

A distinctive feature of this validation process is that “over 80%” of participants had never met the author directly nor disclosed their personal issues in advance. Most were referred by others, thereby reducing bias and ensuring that forecasts were based solely on palm crease analysis rather than contextual knowledge. Moreover, many individuals across these regions took the effort to” voluntarily reach out” after their forecasts to provide feedback, often emphasizing the striking accuracy they experienced.

Finally, the author’s advanced degrees & background in building forecasting models provided a rigorous methodological foundation. By applying world-class practices of testing, refinement, and iterative validation, the framework maintains both scientific discipline and practical accessibility. This combination of scale, diversity, participant feedback, and methodological rigor underscores the model’s “robustness, reproducibility, and potential forensic value”.

Retrospective testing

The model is ideally tested against an individual’s history. Beginning at age 5, known life events are compared against the algorithms' predicted windows. Each comparison is classified as:

Hit: a major event occurred within ±6 months.

Miss: No major event occurred in that window.

Ambiguous: only minor or uncertain events occurred.

Prospective testing

If retrospective testing yields acceptable accuracy [8], the model is used prospectively. Forecasts are generated for the coming decade. Over four decades of application, thousands of individuals participated in this process. Many provided prospective feedback, reporting whether predicted events occurred. This continuous feedback loop enabled iterative refinement of the formulas and offered a rare opportunity for long-term validation.

Application in forensic cases

In forensic contexts, retrospective life histories may not be available, particularly when analysing cadavers or unidentified remains. In such cases, the model can still be applied directly to palm photographs to generate a timeline of predicted transition windows. These predictions can then be tested against any available biographical data or reconstructed histories. Thus, the model remains usable even in the absence of retrospective validation, while continuing to provide falsifiable outputs.

Prior research has examined palm creases primarily through associations with lifespan, congenital anomalies, or forensic outcomes. For example, Lucas6, et al. [9] ^4 and HSPI5 conducted prospective and correlational analyses, concluding that crease length alone is insufficient to predict unnatural death or longevity. These studies underscore the limitations of single-variable approaches. In contrast, the present framework contributes a temporal dimension, generating six-month predictive windows beginning in early childhood. This temporal granularity permits falsifiable testing across life transitions, a feature absent in earlier work. Additionally, by incorporating multi-crease analysis, our method moves beyond linear associations and instead provides probabilistic inference across multiple life domains. Thus, while prior studies identified the boundaries of crease-based predictions, the present model extends the field by offering reproducible, testable forecasts with forensic applicability.

Comparison with prior studies

Dependence on retrospective accuracy, where possible: The model is not universal. If it fails to reconstruct past events within an acceptable range in cases where histories are known, it is not applied prospectively.

Age range: Between ages 5 and 60; both timing and domains can be inferred. Beyond 60, only timing remains reliable.

Crease reliability after 60: Timelines derived from minor creases become inconsistent in later life; forecasts rely primarily on one major crease.

Probabilistic nature of domains: Event content cannot be specified in advance. Domains such as health, relationship, work, or creativity are suggested, but interpretation remains probabilistic.

Retrospective analysis

The model creates opportunities for retrospective forensic study. Where palm photographs and life histories are available, predictions can be compared against known events. This could include deceased individuals with documented biographical timelines.

Improved temporal resolution

Current crease-length studies fail to provide testable temporal precision. This framework introduces a six-month resolution beginning at age 5, permitting meaningful statistical testing of alignment between predicted and actual transitions. This level of granularity is unprecedented in palm crease research and represents a significant methodological advance.

Practical accessibility

Unlike DNA sequencing, which is invasive, costly, and largely retrospective, palm crease forecasting is non-invasive, inexpensive, and applicable to both retrospective and prospective timelines. One of the strengths of the framework is its low technological barrier and short time to get numerical forecasts. Because analysis can be performed on palm photographs using simple measurement tools, it is feasible even in constrained forensic environments. Historical or case records containing palm images could be analysed retrospectively without specialized software or laboratory infrastructure.

Clarification of scope

This framework does not claim causation. Palm creases are not proposed as forces that shape life events. Rather, they are treated as a biological map that correlates with temporal patterns of transition.

The use of “forecasting” is deliberately bounded. Predictions are probabilistic windows of increased likelihood, not certainties. The model is phenomenological: it describes observed regularities without asserting an ultimate mechanism.

It is also not a universal law. The model applies only when retrospective validation succeeds, except in forensic cases where such histories are absent and forecasts may be applied directly. By addressing unbounded human problems, the model situates itself between deterministic laws and probabilistic openness. It offers structure without rigidity, enabling forensic science to test a forecasting framework that respects complexity while remaining falsifiable.

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5. HSPI, 2025
6. Teghan Lucas, Amrita Dhugga, Maciej Henneberg. Predicting longevity from the line of life: is it accurate?2019;82(2). Available from: https://www.researchgate.net/publication/333915416_Predicting_longevity_from_the_line_of_life_is_it_accurate
7. Health, relationship, work, relocation
8. Typically >85% hits