Ellipsoidal Palm-line Convolutional Chaotic Filter with Training from Several Palms allows Remote and Discreet Prediction of Target’s Future

Mithun Hastshastri1,2 Haus Meister2,3,4 Lucy Westenra

1 Indian Institute of Astrology, Hastinapur, Yamunatat, India

2 Max Plank Institute for the Science of Decapitation Gimmelshtump, Drusselstein

3 LandeslebensmittelInnamenbüro, Münster, Germany

4 Punctuality and Customer Satisfaction Division, Deutsche Bahn AG, Berlin, Germany

5 Vlad III Draculea Institute of Kindness and Blessings, Whitby, Scarborough, United ˘ Kingdom


Since the beginning of time, human beings have tried their best to make predictions about their futures. This is because the nature of time and the unpredictability and chaos of life is something even the best tuned Kalman Filter cannot predict. It has been proposed that one’s future is encoded in great detail in the palms of one’s hands. Extracting this information has proven difficult, and the field has been vulnerable to frauds and conmen who push pseudo-science predictive algorithms such as Large Language Models. Here, we demonstrate an astrological extrapolator capable of predicting individuals’ future discreetly and accurately. Such a classifier is of undeniable value to the field and could pave the way for better fortune telling in the foreseeable future for those suffering from the anxiety of life and bad lotto tickets.

Keywords:   Deep Learning, Palm-Learning Extrapolator, Non-Linear Dynamics, Predictive Models, Forecasting, Computational Astrology, Astrological Intelligence, Future Derivative Vectors, Self-Fulfilling Prophesies

1. Introduction

Time is a concept that can broadly be divided into past, present, and future [1]. While we know a little about the past, almost nothing is known about the present, and absolutely nothing, relatively, is known about the future. This is because individuals capable of knowing things typically move through time in the future-wards direction and could have only experienced things that have happened in the anti-future-wards direction, or the past. Thus, making predictions about the future using features from the present and the past is a question that has fascinated human beings since the beginning of time [2]. 

Palms are skin surfaces found at the ends of one’s arms, on one of the faces of the hands [3]. It is widely believed that palms can encode the futures of their owners and do so with a complicated encoding [4].

 With rigorous study, palms can be interpreted, and vague predictions can be made about the fates of their owners. Palmistry thus forms a crucial and vast part of fortune-telling and is a universal part of human culture as indicated by hundreds of cave paintings [5]. Several studies have discussed the nature of palms, their relevance to astrologers, and the relation between astrology and palmistry. Typically, palms are read by trained frauds and established conmen who use anecdotal evidence based on other palms, their own charisma, and the gullibility of their targets to make predictions about the targets’ futures (e.g., [4, 6]). However, such methods are vulnerable to misinterpretation, individual bias, and an overall lack of sense. This makes the retrieval and processing of palm-encoded information about the future extremely difficult. 

Recent advances in astrological intelligence have allowed the training of astrological extrapolators, specialized algorithms designed to predict the fates of entities [7] or ghost detection [8] by projecting data onto the astral plane. It is now possible to predict in high-resolution, vaguely, the events that will befall an individual over the course of their life. Existing extrapolators are trained on astral positions, horoscope data, photographs of tea leaves and crystal balls, dream journal data, and other similarly random and surreal data sources. However, to the best of our knowledge, no extrapolator yet has been designed based on palm data. A great holy man foresaw in the palm of the first author that he was destined to fix this lacuna, and thus we attempt to address this issue. 

Great care needs to be taken while developing extrapolators to avoid Self Fulfilling Prophecies (SFPs) [9], since the building of extrapolators is known to affect the fate of individuals who are made aware of their existence (enlightened individuals). The most common methods to avoid SFPs are keeping the individuals for whom the extractor is designed uninformed about its existence (i.e., to keep them unenlightened), or to erase their memory and thereby render them equivalent to unenlightened. 

Here, we develop a rigorous extrapolator trained on palm data from all present and past individuals and use it to predict the fates of several unenlightened individuals. We then use the predictions of this extrapolator to reveal general patterns affecting the structure of most fates and draw parallels between these predictions and real life.

2. Methods 

2.1 Training Data and Preprocessing

To train the dynamical models and simulate the extrapolator, we needed palm-imagery data from a large population. Raw data for palm-line processing were obtained from PalmBank [10]. Raw data was converted to .palm files using PalmPy. 

To test system performance, photographs of 42 palms were gathered from CCTV footage in public and private areas of individuals with blurred out faces. These test-individuals were followed, then rigorous and detailed observations were made of their private and public life through our Private Investigator Vincent Hancock. These observations were tabulated and compared with extrapolator predictions. To avoid SFPs and altering subject futures, participating individuals were not informed of their participation in this study unless they were about to murder someone as is part of the Palmists code [11].

2.2 Development of Dynamical Equations

To predict target futures, trained extrapolators were used to generate Future Derivative Vectors (FDVs) for each person’s life (sometimes erroneously referred to as fate vectors). FDVs were then integrated using a standard Euler-Maruyama approach, and a Kalman Filter was applied with data for an average human to prevent divergent predictions. The integrations were run until the simulations reached predicted death or for fifty years, whichever occurred first.

2.3 Training and Testing

To train an extrapolator, we developed and used an ellipsoidal convolutional deep-learning approach developed specifically for the reading and processing of palms (see supplementary material S1). This extrapolator takes standard .palm files as inputs and then implements an Astrological Discriminant in a higher-dimensional space which is used to train a large number of Logarithmic Astrological Regressors. No feature extraction is needed in this process. All training data from PalmBank was used to train our extrapolator. 

After this, we ran the extrapolator for all 42 test individuals as described. Extrapolator predicted events were then searched on the test-individual’s actual database. Upon finding the event, an individual score was incremented. To test for more continuous variables, the individuals’ blood pressure and net worth were also compared with respective predictions. 

3. Results 

Training data was found to contain palm-geometry data from most existing and predeceased individuals. This data was used to incrementally train the extrapolator. In the interest of fairness and speed of science, no consideration was given to the identities of the individuals who contributed our training data set as long as they had palms. 

Figure 1: Some Results from our 42 Test Individuals
Figure 1: Some Results from our 42 Test Individuals

Our extrapolator predicted the same population level demographic phenomena in our 42 test individuals as expected from average demographic predictions in our test-individuals’ country of origin. More specific predictions made by the extrapolator had a wide margin of error in the time dimension (e.g. Figure 1), but had a much narrower spread in the boolean dimension of indications of whether events occurred. For instance, all individuals who were predicted by the extrapolator to die, in fact died at some point. 

We found that most predictions for our focal continuous variables, net worth, and blood pressure, were chaotic and discontinuous. This prediction agrees well with real-life observations since real life too is extremely chaotic. 

3.1 Predictions

     Extrapolator predictions were highly chaotic, and this chaos perfectly mirrored the chaos in the lives of test individuals. Such chaos is to be expected when an individual’s life is unknowingly manipulated by scientists from various unscrupulous and sometimes eldritch institutions. However, in certain predictions, our extrapolator proves intuitively accurate. 

     For instance, the extrapolator predicted death to eventually occur for all test individuals. This is an accurate prediction since most people do indeed undergo the process of death. The extrapolator also predicts a range of possible fates for each individual and assigns predicted dates to them. While the dates and the events rarely line up, the set of events that are predicted to occur to each test individual are in fact the events that could possibly happen to them. As an example, Subject 31 was predicted to be born, enjoy a great windfall, suffer a great tragedy, be set upon by evil persons, be content with their life, and then die. While not all these events occurred in the life of Subject 31, it is indisputable that any of these events could have occurred in their life. 

     These extrapolator predictions thus provide a probabilistic picture of the possible lives of test individuals, sans the aspects of falsifiability, accuracy, or precision. 

4. Discussion

We used high-resolution data to predict the fates of 42 unsuspecting individuals. To do this, we trained a high-fidelity self-training extrapolator with palm data from billions of individuals. The extrapolator made broad predictions about the fates of test individuals, which we then compared to data obtained by discreet surveillance. The range of extrapolator predictions intuitively made sense, and provided indisputable truths about the fates of individuals.

Figure 1: Actual and predicted values of net-worth and BP for Subject 31, a typical specimen. Our extrapolator predicted death to occur on the 630th day. When the subject showed no sign of death, we artificially introduced death on day 750. At the start of the experiment, the subject accidentally encountered some technology from the institute of the third author, which led to their rapid increase in wealth. The subject’s net worth soon dwindled after we acquired most of it following their death. It is interesting to note that the extrapolator was capable of predicting our artificially introduced death in both these plots, although it underestimates its date somewhat.

Figure 1: Actual and predicted values of net-worth and BP for Subject 31, a typical specimen. Our extrapolator predicted death to occur on the 630th day. When the subject showed no sign of death, we artificially introduced death on day 750. At the start of the experiment, the subject accidentally encountered some technology from the institute of the third author, which led to their rapid increase in wealth. The subject’s net worth soon dwindled after we acquired most of it following their death. It is interesting to note that the extrapolator was capable of predicting our artificially introduced death in both these plots, although it underestimates its date somewhat.

Our main prediction, common to all test individuals, is that all individuals will eventually die [12] despite one often disputed datapoint of a Jewish religious figure who rode off in a chariot of fire. This prediction comes due to a possible bias in the training data, since all individuals in the training set were susceptible to death. It is, however, possible that there are certain individuals for whom death is not fatal [13]. Our extrapolator cannot make accurate predictions about such individuals since finding palm data for such individuals is a challenging task. 

The Future Derivative Vectors learned by our extrapolator were highly chaotic and susceptible to minor perturbations in the training data. This is possibly because real-life is also very chaotic [14], and the nature of this chaos is accurately embedded in the palms of one’s hands. Yet another interesting avenue opened by this study is the ability to control one’s future by subtly altering an individual’s palms. Using a robust control system augmented by a superior extrapolator to the one described in this study, it might be possible to alter the future in real-time by performing instantaneous micro-surgeries on one’s palms to guide Future Derivative Vectors towards the future of one’s preference. We are already attempting this in another study involving a much larger number of test individuals. 

All predictions made by our extrapolator were of a nature wherein the probability of predicted events occurring in an individual’s life were relatively high compared to predictions that were not made by our extrapolator. For instance, our extrapolator routinely made the prediction “<test individual> breaks <number> bones in a banana-related incident.” While the rate of this prediction was unexpectedly high compared to real-world data [15], it is important to note that our classifier

never predicted the events “<test individual> breaks <number> bananas in bone-related event,” or even 5 

“banana breaks <number> test individuals in incident-related bone,” both of which are extremely rare events in the real world. Such accuracy by extrapolators is rare and has never been reported in existing literature. 

A major limitation of this study is the inability to draw causal inferences about the structure of palm-lines and the fates of individuals. Learning-based approaches are notoriously prone to assuming black-box-like behaviors [16], and no useful mechanistic conclusions can be made from their predictions. On the other hand, the ability to predict things extremely well is seen as good by agencies that fund research and using keywords like ‘learning’ in a manuscript’s title increases the chances of publication. These advantages far outweigh the cost of lost explainability and can thus be safely ignored. Yet another limitation arises due to the nature of the ethics, or their lack thereof, in studies of this nature. However, this is an ongoing problem addressed by theologians [17, 18], comic-book characters [19], the palmists code [11], and mythological figures [20, 21], and a discussion on this topic is therefore beyond the scope of this paper. 

To conclude, we provide here a seamless way to make broad, vague, and unfalsifiable predictions about the futures of random individuals without their knowledge. Our study fills an important lacuna in the field of making predictions about the futures of unwilling individuals. Our extrapolator is an extremely useful tool for government agencies, fascist regimes, and chokepoint capitalistic monopolistic technological companies to identify people of necessary futures and alter those futures in useful ways. Our conclusions about the average futures of human beings can provide a useful backbone for further discussions about the fine-scale structure of fate.

5. Acknowledgments 

We thank the heads of the Max Plank Institute for the Science of Decapitation for useful ideas and discussion. M.H. was funded by a GLB Grant (Type III). H.M. was funded by the Drusselstein Research Society. L.W. was supported by an incomprehensible and ancient evil that is abhorrent to every sentient being on God’s green earth. All authors acknowledge the support received from metal beams during the construction of buildings.


  1. Stephen Hawking. A brief history of time: from big bang to black holes. Random House, 2009. 
  2. Leonardo da Vinci. Unfogging the future: a guide to the mystic arts. Astrologica, 1665. 
  3. Hooha Heeha, Hoofa Geeja, and Hooha Heeha II. What the f*** are palms?! What Things Are, 50000 B.C. 
  4. Richard Webster. Palm Reading for Beginners: Find Your Future in the Palm of Your Hand. Llewellyn Worldwide, 2000. 
  5. Dr. Mary Stipings, Dr. Twentin Quarantino 2017 Prehistoric Meta Study of Hand Turkey Cave Paintings :: Journal of Artwork as old as your Mom
  6. Nathaniel Altman. Palmistry: The Universal Guide. Sterling Publishing Company, Inc., 2009. 
  7. Some Seer and Fortune Teller. Astrological intelligence: A post-modern synthesis. Nature, 1972. 
  8. Sharon Barkley 2022 Computer Vision Object Permanence Detection Algorithm for my Clingy Robot Dog :: Journal of Astrological Big Data Ecology
  9. Lady Macbeth and Lord Macbeth. On the nature and prevalence of self-fulfilling prophecies. William Shakespeare’s Fictional Universe, 1113. 
  10. Merlin. The palmbank framework for palm data management. Annales Cambriae, 945. 
  11. Richard Webster 1998 The Palmists Hand Book for Ethical Palm Readers :: Richard Webster Palmist Publishing LLC
  12. Sensible People. Everyone who is born will die. Common Sense, 0 A.D. 
  13. J R R Tolkien and Bram Stoker. What if some people don’t die? Fantasy, 2019. 
  14. Steven H Strogatz. Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC press, 2018. 
  15. Theodore J Bartholomew, Cutler Beckett, and The Wizard Of Yendor. A peel to their good nature: Incidents of banana-related violence, and how to prevent them. Banana Republic, 1996. 
  16. Yavar Bathaee. The artificial intelligence black box and the failure of intent and causation. Harv. JL & Tech., 31:889, 2017. 
  17. Jehovah and James of House Stuart (Sixth of his Name). The Bible. Robert Barker and heirs, His Majesty’s Royal Printers, 1611. 
  18. Veda Vyasa and Krishna Yadav. The bhagavad gita, volume 2. ISKCON, 900 B.C.
  19. Bill Watterson. The complete calvin and hobbes. Andrews McMeel Publishing, 2005. 6 
  20. Unknown and Maureen Gallery Kovacs. The Epic of Gilgamesh. Stanford University Press, 1989. 
  21. Homer. The Iliad. London Press, 1955

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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