Prediction

For other uses, see Prediction (disambiguation).

• "predict" redirects here. For other uses, see predict (disambiguation).

The Old Farmer's Almanac is famous in the US for its (not necessarily accurate) long-range weather predictions. A rather endearing, if often misguided, human endeavor.

A prediction, derived from the Latin præ- ("before") and dictum ("something said") ¹, or a forecast, is fundamentally a statement concerning a future event or about data that has yet to manifest. More often than not, these pronouncements are rooted in the accumulated experience or specialized knowledge of those doing the foretelling. However, the precise delineation between what constitutes a "prediction" and what is merely an "estimation" remains a point of contention; various authors and academic disciplines are quite fond of assigning their own distinct connotations to these terms, as if the universe cares for such semantic squabbles.

Given that future events are, by their very nature, steeped in uncertainty, the notion of acquiring guaranteed accurate information about what is to come is, quite frankly, an impossibility. Yet, despite this inherent limitation, the act of prediction can prove remarkably useful, particularly when it comes to crafting plans that attempt to account for a myriad of potential future developments. It's a rather optimistic exercise, really, considering the odds.

Opinion

In a context that steers clear of statistical rigor, the term "prediction" frequently serves as a polite descriptor for what is essentially an informed guess or opinion. One might call it an educated gamble.

Such a prediction might draw its tenuous strength from a predicting individual's capacity for abductive reasoning, inductive reasoning, or deductive reasoning, all filtered through their unique lens of experience. Its utility, naturally, hinges entirely on whether the person making the prediction is genuinely a knowledgeable person in the specific domain in question. Unfortunately, this is a rather rare commodity. ²

The Delphi method stands as a structured technique specifically designed to extract these expert-judgement-based predictions in a somewhat controlled environment. It's an attempt to wrangle collective human intuition into something resembling a coherent outlook. This particular brand of prediction can, in a stretch, be seen as compatible with more traditional statistical methodologies, in the sense that, at its most basic, the "data" being processed is the predicting expert's intricate tapestry of cognitive experiences, which, at some subconscious level, forms an intuitive "probability curve." A rather messy curve, I imagine.

Statistics

Within the austere realm of statistics, prediction occupies a significant space as a core component of statistical inference. While one specific methodological approach is even christened predictive inference, the act of prediction itself can be executed across any of the various established frameworks for statistical inference. Indeed, one could argue that a fundamental description of statistics is its provision of mechanisms to extrapolate knowledge gleaned from a mere sample of a given population to the entirety of that population, and even to other related populations. This is not necessarily synonymous with prediction across a timeline. When such information is indeed extrapolated across time, often targeting specific future moments, the entire process takes on the more specialized designation of forecasting. ³ This distinction is not merely academic; forecasting typically necessitates the deployment of sophisticated time series methods, whereas prediction is frequently applied to analyses involving cross-sectional data.

The statistical arsenal employed for prediction is vast and varied, encompassing techniques like regression and its numerous sub-categories. These include, but are certainly not limited to, linear regression, the broader class of generalized linear models (which includes such luminaries as logistic regression, Poisson regression, and Probit regression), among others. For the specific demands of forecasting, models such as autoregressive moving average models and vector autoregression models often take center stage. When this intricate web of regression and/or related, more generalized regression or machine learning methodologies are deployed for commercial applications, the field is elegantly termed predictive analytics. ⁴

In a multitude of practical applications, especially within the domain of time series analysis, it becomes feasible to construct and estimate models that are believed to be responsible for generating the observed data. Should these underlying models be articulated as transfer functions or expressed through state-space parameters, then it becomes possible to compute smoothed, filtered, and ultimately, predicted data estimates. ⁵ If the generative models beneath these observations are conveniently linear, then a minimum-variance Kalman filter and a corresponding minimum-variance smoother can be effectively utilized to extract the data of interest from what are invariably noisy measurements. These techniques critically depend on what are known as one-step-ahead predictors, which are designed to minimize the variance of the prediction error. However, when confronted with the untidy reality of nonlinear generating models, one must resort to applying stepwise linearizations within the recursive frameworks of the Extended Kalman Filter and its associated smoother. A crucial caveat here: in these nonlinear scenarios, the coveted guarantees of optimum minimum-variance performance unfortunately cease to apply. ⁶

To harness the power of regression analysis for the purpose of prediction, a meticulous collection of data is undertaken. This data pertains to the variable slated for prediction, known as the dependent variable or response variable, as well as one or more variables whose values are hypothesized to exert an influence upon it—these are the independent variables or explanatory variables. A specific functional form, frequently linear for simplicity, is then posited for the hypothesized causal relationship. Subsequently, the parameters of this function are estimated from the collected data; that is, they are carefully selected to optimize, in some predefined manner, the fit of the parameterized function to the data. This constitutes the estimation phase. For the actual prediction phase, values for the explanatory variables—values that are deemed pertinent to future (or currently unobserved) states of the dependent variable—are fed into the now parameterized function to generate the desired predictions for the dependent variable. ⁷ It's a rather elaborate dance, but sometimes, it works.

Machine learning and artificial intelligence

In recent decades, the act of prediction has ascended to a position of paramount importance, becoming a central driving force in both machine learning and artificial intelligence research. It's as if humanity finally built machines to do the guessing for us.

Supervised learning algorithms, a class of models that includes such luminaries as support vector machines, decision trees, and neural networks, are meticulously trained on vast historical datasets. Their purpose? To discern patterns and subsequently predict outcomes when presented with novel, previously unseen data. These sophisticated models have found widespread application across an impressive array of domains, including the intricate world of natural language processing, the visual complexities of computer vision, the critical field of health informatics, and the ever-evolving landscape of financial technology. Recent academic discourse has placed significant emphasis on the crucial importance of model interpretability and fairness, a rather human concern, given that these predictions can wield considerable influence over critical decisions in areas ranging from healthcare and criminal justice to public policy. ⁸

To obtain an unbiased assessment of a model's performance, it is standard practice to evaluate its predictions against dedicated hold-out test sets. The resulting predictions can then be visually juxtaposed with the actual ground truth, often through the use of a parity plot, offering a clear, if sometimes disheartening, picture of accuracy.

Science

NASA's 2004 predictions of the solar cycle, which were inaccurate (predicting that solar cycle 24 would start in 2007 and be larger than cycle 23), and the refined predictions in 2012, showing it started in 2010 and is very small. Even the best minds get it wrong sometimes, or often.

In the realm of science, a prediction transcends mere speculation; it is a rigorous, and frequently quantitative, declaration, positing precisely what ought to be observed under a set of specific, predefined conditions. For instance, according to the established theories of gravity, if an apple were to detach itself from a tree, it would invariably be observed to accelerate towards the Earth's core with a specified and constant rate of acceleration. The very bedrock of the scientific method is constructed upon the systematic testing of statements that emerge as the logical consequences of existing scientific theories. This critical validation process is carried out through meticulously designed and repeatable experiments or through careful observational studies.

A scientific theory whose predictions consistently clash with empirical observations and evidence is, quite rightly, destined for rejection. Conversely, nascent theories that successfully generate a multitude of fresh, testable predictions often find it easier to gain robust support or, just as crucially, to be definitively falsified. This capacity to generate new, testable implications is often referred to as a theory's predictive power. Concepts or propositions that fail to yield any discernible testable predictions are generally relegated outside the boundaries of established science, often categorized as protoscience or even nescience, until such a time as verifiable predictions can, if ever, be formulated.

Mathematical equations and models, alongside sophisticated computer models, are routinely deployed to elucidate the past and anticipate the future behavior of a particular process, always operating within the inherent constraints and boundaries of that model. In certain instances, particularly within the perplexing domain of quantum physics, it is the probability of a given outcome, rather than a singular, definitive outcome, that can be predicted.

Within the intricate architecture of microprocessors, a technique known as branch prediction is utilized to circumvent the performance penalties associated with emptying the pipeline when encountering branch instructions, thus optimizing computational flow.

In the practical field of engineering, potential failure modes are meticulously predicted, allowing for proactive avoidance by identifying and rectifying the underlying failure mechanism before it can cause actual malfunction.

It is worth noting that accurate prediction and forecasting prove to be exceptionally challenging in certain complex domains. These include, but are hardly limited to, the chaotic realm of natural disasters, the unpredictable spread of pandemics, the intricate shifts in demography and population dynamics, and the ever-fickle science of meteorology. ⁹ For instance, while the recurrence of solar cycles can be predicted with reasonable certainty, their precise timing and specific magnitude remain considerably more elusive, as evidenced by the adjustments made to NASA's solar cycle predictions (see the image to the right)—a testament to the stubborn unpredictability of certain cosmic rhythms.

In the specialized field of materials engineering, it is also feasible to predict the operational life time of a material through the application of a suitable mathematical model. ¹⁰

Furthermore, in medical science, the identification and utilization of predictive and prognostic biomarkers can serve a critical function, allowing practitioners to anticipate patient outcomes in response to various treatment regimens or to gauge the likelihood of a specific clinical event. ¹¹

Hypothesis

Established scientific disciplines are capable of generating profoundly useful predictions, which are frequently characterized by their extreme reliability and accuracy; for example, the precise timing and visibility of eclipses are routinely predicted with impressive fidelity.

Crucially, nascent theories put forth predictions that inherently allow for their refutation by empirical reality. Consider, for instance, the current research challenge of accurately predicting the structure of crystals at the atomic level. ¹² In the early 20th century, the prevailing scientific consensus posited the existence of an absolute frame of reference, which was rather grandly dubbed the luminiferous ether. The hypothesized existence of this absolute frame was deemed indispensable for maintaining consistency with the then-established principle that the speed of light remained constant. However, the renowned Michelson–Morley experiment conclusively demonstrated that the predictions rigorously deduced from this concept simply did not materialize in reality, thereby effectively disproving the theory of an absolute frame of reference. It took Albert Einstein's groundbreaking special theory of relativity to propose an elegant explanation for the apparent inconsistency between the constancy of the speed of light and the undeniable non-existence of a special, preferred, or absolute frame of reference.

Albert Einstein's subsequent theory of general relativity presented a more formidable challenge for early experimental verification, primarily because it did not readily produce effects observable on a terrestrial scale. Nevertheless, as one of the very first tests of general relativity, the theory boldly predicted that immense masses, such as stars, would cause light to bend in their vicinity—a phenomenon that directly contradicted the then-accepted understanding of light's behavior. This profound prediction was strikingly observed during a solar eclipse in 1919, providing compelling empirical evidence for the theory.

Medicine and healthcare

Predictive medicine

• This section is an excerpt from Predictive medicine.[edit]

Predictive medicine represents a specialized field within medicine that meticulously involves foretelling the probability of an individual developing a particular disease. The ultimate aim is to subsequently implement proactive preventive measures, either to entirely avert the onset of the disease or to substantially mitigate its potential impact upon the patient. Such mitigation could manifest as preventing mortality or significantly curtailing morbidity. ¹³

While a diverse array of prediction methodologies exists, including advanced techniques rooted in genomics, proteomics, and cytomics, the most foundational and often earliest means of forecasting future disease susceptibility is, quite simply, based on an individual's genetic makeup. Although proteomics and cytomics can facilitate the early detection of disease, these methods often identify biological markers that are present precisely because a disease process has already commenced, even if subtly. In contrast, comprehensive genetic testing, achieved through sophisticated DNA arrays or full genome sequencing, offers the remarkable ability to estimate disease risk many years, even decades, before any symptoms of the disease even appear. It can even ascertain whether a healthy fetus carries a heightened risk for developing a specific condition later in adolescence or adulthood. Individuals identified as being more susceptible to future diseases can then be provided with tailored lifestyle advice or prophylactic medication, all with the explicit goal of preventing the predicted illness from ever manifesting.

Current guidelines for genetic testing, robustly endorsed by healthcare professionals, generally discourage purely predictive genetic testing for minors. This stance is maintained until such individuals achieve sufficient cognitive maturity to fully grasp the profound relevancy of genetic screening, thereby enabling them to actively participate in the weighty decision of whether such testing is appropriate for them. ¹⁴ Nevertheless, genetic screening of newborns and children within the framework of predictive medicine is deemed appropriate under compelling clinical circumstances, particularly when there exists an available prevention or treatment that, if administered during childhood, would effectively avert a debilitating future disease.

Prognosis

• This section is an excerpt from Prognosis.[edit]

Prognosis, a term derived from the Ancient Greek prógnōsis meaning "fore-knowing" or "foreseeing," is a critical medical concept. It encapsulates the act of predicting the probable trajectory or anticipated evolution of a disease. This includes assessing whether the signs and symptoms are likely to improve, worsen (and at what rate), or remain stable over time. Furthermore, it encompasses expectations regarding the patient's quality of life, such as their capacity to perform daily activities, the potential for complications and associated health issues, and, most critically, the likelihood of survival, including an estimate of life expectancy. ^{15 16} A comprehensive prognosis is formulated by taking into account the typical course of the diagnosed disease, the individual patient's unique physical and mental condition, the spectrum of available treatments, and any additional relevant factors. ¹⁶ A truly complete prognosis extends beyond mere survival rates, offering an expected duration of the illness, a projection of functional capacity, and a descriptive outline of the disease's anticipated course—be it a progressive decline, a pattern of intermittent crises, or a sudden, unpredictable onset of severe events. ¹⁷

When applied to expansive statistical populations, prognostic estimates can achieve a remarkable degree of accuracy. For example, a statement such as "45% of patients diagnosed with severe septic shock will unfortunately die within 28 days" can be asserted with a significant level of confidence. This confidence stems from previous rigorous research that has consistently observed this specific proportion of patients succumbing to the condition. However, it is crucial to understand that this statistical information, while robust for a population, does not directly translate to the prognosis for any single individual patient. Patient-specific factors can introduce substantial variability, dramatically altering the expected course of the disease. Therefore, additional, individualized information is always indispensable to determine whether a particular patient falls into the 45% who will die, or into the 55% who will, against the odds, survive. ¹⁸

Clinical prediction rules

• This section is an excerpt from Clinical prediction rule.[edit]

A clinical prediction rule, also referred to as a clinical probability assessment, provides explicit instructions on how to use medical signs, symptoms, and various other clinical findings to estimate the probability of a specific disease or a particular clinical outcome. ¹⁹ It's an attempt to bring some order to the inherently chaotic nature of human biology.

It has been consistently observed that physicians often encounter considerable difficulty in accurately estimating the risks associated with various diseases, frequently erring towards overestimation. ²⁰ This tendency may be, in part, attributable to well-documented cognitive biases, such as the base rate fallacy, in which the perceived risk of an adverse outcome is disproportionately exaggerated, making the human mind a rather unreliable instrument for pure probability assessment.

Finance

Main articles: Financial forecasting and Stock market prediction

Prediction market

Mathematical models purporting to describe stock market behavior (and, by extension, economic behavior in general) possess a rather unfortunate track record when it comes to reliably predicting future trends. Among the myriad reasons for this persistent unreliability is the inconvenient truth that significant economic events often unfold over spans of several years, during which the global landscape itself undergoes continuous transformation. This dynamic invalidates the direct relevance of past observations to the present, leaving an extremely limited number—often on the order of one—of truly pertinent historical data points from which to project the future. Furthermore, it is a widely held belief that current stock market prices already reflect and incorporate all publicly available information relevant to future prospects. Consequently, any subsequent movements must, by definition, be the product of genuinely unforeseen events. This makes it extraordinarily difficult for a typical stock investor to accurately anticipate or predict either a stock market boom or a devastating stock market crash. In stark contrast to the often futile endeavor of predicting actual stock returns, the forecasting of broader economic trends tends to exhibit a comparatively superior level of accuracy. Such analyses are routinely provided by both non-profit organizations and by a multitude of for-profit private institutions. ²¹

Intriguingly, some degree of correlation has been observed between actual stock market movements and prediction data derived from large groups participating in surveys and specialized prediction games. It seems collective human sentiment, however flawed, can sometimes align with reality.

An actuary is a professional who applies the principles of actuarial science to meticulously assess and predict future business risk, with the express purpose of ensuring that these identified risk(s) can be effectively mitigated. For instance, in the domain of insurance, an actuary would consult and utilize a life table—a comprehensive statistical tool that synthesizes historical mortality rates and often incorporates an informed estimate of future demographic trends—to project life expectancy for various population segments.

Sports

The prediction of sporting event outcomes has, in recent years, blossomed into a burgeoning and increasingly popular industry. Professionals known as handicappers meticulously predict the results of games, employing a diverse toolkit that includes intricate mathematical formulas, sophisticated simulation models, or rigorous qualitative analysis. Historically, early and well-known sports bettors, such as the legendary Jimmy the Greek, were widely believed to possess privileged access to insider information that afforded them a critical edge. This information could range from deeply personal issues, such as a player's gambling habits or struggles with alcohol, to undisclosed injuries—essentially anything that might subtly, or overtly, compromise a player's performance on the field.

Recent times have ushered in a significant transformation in the methodologies employed for sports prediction. Modern predictions typically coalesce around two distinct approaches: "situational plays" and sophisticated statistical models. Situational plays, by their very nature, are considerably more challenging to quantify, as they often delve into the less tangible realm of a team's motivation and psychological state. Dan Gordon, a widely recognized handicapper, famously articulated this sentiment, stating, "Without an emotional edge in a game in addition to value in a line, I won't put my money on it." ²² These types of plays encompass a variety of scenarios, such as betting on the home underdog, wagering against Monday Night Football winners who are favored the following week, or backing the underdog in so-called "look ahead" games. However, as these situational plays become more widely disseminated and understood, their utility diminishes, as they are gradually factored into the initial setting of the betting line.

The pervasive integration of technology has, perhaps inevitably, given rise to more modern sports betting systems. These systems are typically manifested as complex algorithms and intricate simulation models, often built upon the foundations of regression analysis. Jeff Sagarin, a prominent sports statistician, garnered significant attention in the sports world by having the results of his predictive models regularly published in USA Today. He currently serves as a paid consultant for the Dallas Mavericks, offering his expert advice on team lineups and the application of his proprietary Winval system, which meticulously evaluates free agents. Similarly, Brian Burke, a former United States Navy fighter pilot who transitioned into a career as a sports statistician, has published his findings on utilizing regression analysis to predict the outcomes of NFL games. ²³ Ken Pomeroy is broadly acknowledged as a leading authority in college basketball statistics; his website hosts his acclaimed College Basketball Ratings, a statistics system specifically designed around game tempo. The success of certain statisticians in developing highly effective prediction systems has propelled them to considerable fame within the industry. As Dare astutely observed, "the effective odds for sports betting and horse racing are a direct result of human decisions and can therefore potentially exhibit consistent error." ²⁴ This inherent characteristic distinguishes prediction in sporting events from other casino games, lending it a unique potential for both logical and consistent analytical approaches.

Other, more advanced models integrate concepts such as Bayesian networks, which are essentially causal probabilistic models frequently employed for sophisticated risk analysis and decision support. Building upon this sophisticated mathematical modeling framework, Constantinou et al. ^{25 26} have developed models specifically designed for predicting the outcomes of association football matches. ²⁷ What renders these models particularly compelling is their capacity to not only incorporate relevant historical data but also to integrate those often "vague subjective factors" that elude precise quantification—elements such as the availability of key players, the cumulative effects of team fatigue, or the subtle nuances of team motivation. These models empower the user to input their best educated guesses regarding factors for which hard data may be scarce. This additional subjective information is then synergistically combined with objective historical facts to generate a revised and, ideally, more accurate prediction for future match outcomes. The initial results derived from these modeling practices have been encouraging, demonstrating consistent profitability when pitted against published market odds.

Today, the landscape of sport betting has transformed into a colossal global business. Alongside the myriad betting sites, there exists a vast ecosystem of websites (often referred to as "systems") that provide tips or predictions for upcoming games. ²⁸ While some of these prediction websites, or "tipsters," rely on human intuition and analysis, others leverage sophisticated computer software, colloquially known as prediction robots or "bots." These prediction bots can harness varying amounts of data and employ diverse algorithms, a factor that directly influences their ultimate accuracy.

In recent times, with the relentless advancement of artificial intelligence, the creation of more consistent and reliable predictions through statistical means has become increasingly feasible. Particularly within the dynamic arena of sports competitions, the influence of artificial intelligence has fostered a noticeable improvement in consistency rates. As an illustration of AI's burgeoning prowess in soccer predictions, an initiative named soccerseer.com, which stands out as one of the most successful systems in this specialized niche, reportedly manages to predict the results of football competitions with an accuracy rate of up to 75% by harnessing the power of artificial intelligence. It seems even human folly can be systematized.

Social science

Prediction within the non-economic branches of the social sciences diverges considerably from the methodologies typically employed in the natural sciences. This field embraces a multitude of alternative methods, including rudimentary trend projection, more elaborate forecasting techniques, the development of detailed scenario-building exercises, and structured Delphi surveys. The oil company Shell, for instance, has gained particular renown for its pioneering and extensive scenario-building activities. ²⁹

One fundamental reason for the distinct nature of societal prediction lies in a crucial reflexive dynamic: in the social sciences, "predictors are part of the social context about which they are trying to make a prediction and may influence that context in the process." ³⁰ As a direct consequence of this intricate feedback loop, societal predictions possess the unique capacity to become self-destructing. Consider, for example, a forecast that a substantial percentage of a given population is projected to become HIV infected based on existing trends. The public dissemination of such a dire prediction might paradoxically prompt more individuals to adopt safer behaviors and avoid risky situations, thereby effectively reducing the actual HIV infection rate and, in turn, invalidating the initial forecast (which might have remained accurate had it not been publicly known). Similarly, a prediction asserting that cybersecurity is poised to become a major societal issue could galvanize organizations to implement more robust cybersecurity measures, thereby proactively limiting the very issue that was predicted. ³⁰ It's almost as if humans can't help but interfere with their own future.

Approval ratings (percentages) for the 2004 Canadian federal election. A fleeting snapshot of collective sentiment.

In the sphere of politics, it is a common practice to attempt to predict the outcome of elections through various political forecasting techniques, or to gauge the popularity of individual politicians by employing sophisticated opinion polls. Furthermore, prediction games have been strategically utilized by a diverse range of corporations and governmental entities as a means to gain insights into the most probable outcomes of future events, tapping into collective wisdom, or lack thereof.

Prophecy

Main article: Prophecy

Throughout history, from antiquity to the present day, predictions have frequently been delivered through rather unconventional means, often invoking paranormal or supernatural forces, such as direct prophecy or the meticulous observation of omens. A vast array of methods, including the dubious practice of water divining, the celestial charts of astrology, the numerical mysticism of numerology, the theatrical displays of fortune telling, the often perplexing interpretation of dreams, and countless other forms of divination, have been employed for millennia in humanity's ceaseless quest to peer into the future. It is worth noting, with a distinct lack of surprise, that these particular means of prediction have consistently failed to be substantiated by rigorous scientific experiments.

In the realm of literature, "vision" and "prophecy" serve as potent literary devices, deployed to present a possible timeline of future events to the reader. They can be subtly distinguished by "vision" referring to what an individual character personally perceives or witnesses happening. The Book of Revelation, for instance, within the New Testament, frequently employs vision as a central literary device in this regard. Conversely, it is categorized as prophecy or prophetic literature when such future insights are relayed by an individual in a sermon or through some other public forum.

Divination is broadly defined as the deliberate attempt to acquire insight into a specific question or situation by engaging in an occultic, standardized process or ritual. ³¹ It constitutes an integral component of witchcraft and has been practiced in various forms for thousands of years across diverse cultures. Diviners typically ascertain their interpretations of how a querent (the person seeking answers) should proceed by meticulously reading signs, interpreting events, or deciphering omens, or, alternatively, through alleged direct contact with a supernatural agency—most commonly described as an angel or a god, though often viewed by Christians and Jews as a fallen angel or demon. ³²

Artificial intelligence in astrology

In the 21st century, the ancient practice of astrology has found itself increasingly intersecting with modern advancements in artificial intelligence (AI) and machine learning. Academic research, perhaps surprisingly, has begun to explore the potential utility of AI models for astrological predictions, ³³ while various media outlets have taken note of the emerging trend of "astro-tech" startups and applications. These platforms leverage chatbots and sophisticated algorithmic models to deliver highly personalized astrological readings, a rather telling convergence of ancient superstition and cutting-edge technology. ^{34 35 36}

Notable examples include AI-driven astrology chatbots such as KundliGPT, which employ advanced natural language processing capabilities to generate detailed birth chart readings and respond to user queries in a conversational manner. [^37] It's almost efficient.

Fiction

Fiction, particularly within the genres of fantasy, forecasting, and science fiction, frequently features compelling instances of prediction achieved through means that are, shall we say, rather unconventional. Indeed, the science fiction of yesteryear notably predicted various modern technologies that are now commonplace, demonstrating a peculiar prescience.

In fantasy literature, predictions are often obtained through the mystical workings of magic or the pronouncements of prophecy, sometimes drawing upon ancient traditions and lore. For example, in J. R. R. Tolkien's epic saga, The Lord of the Rings, many of the characters exhibit an uncanny awareness of events extending into the future. This awareness manifests variously as explicit prophecies, or sometimes as more-or-less vague, unsettling 'feelings.' The enigmatic character Galadriel, moreover, employs a magical water "mirror" that reveals images, some of which depict possible future events.

In several of Philip K. Dick's thought-provoking stories, genetically altered humans, known as precogs, possess the extraordinary ability to foresee the future, with their range extending from mere days to many years. In his story titled The Golden Man, an exceptional mutant precog can predict the future to an indefinite range—presumably until his own demise—thereby becoming utterly non-human, an almost animalistic entity that automatically follows its predetermined, predicted paths. Precogs also play an absolutely essential role in another of Dick's seminal works, The Minority Report, which was later adapted into a successful film by Steven Spielberg in 2002.

In the sprawling Foundation series by Isaac Asimov, a brilliant mathematician makes the astonishing discovery that historical events (down to a certain level of detail) can be theoretically modeled using complex mathematical equations. He then dedicates years of his life to transforming this theory into a practical science. The new science of psychohistory, founded upon his success, gains the power to simulate the vast sweep of history and extrapolate the present trajectory far into the future, predicting the rise and fall of entire civilizations.

In Frank Herbert's intricate sequels to his 1965 masterpiece Dune, his characters grapple with the profound and often burdensome repercussions of being able to perceive multiple possible futures and, critically, to select among them. Herbert portrays this ability as a perilous trap leading to stagnation, and his characters are compelled to embark upon a so-called "Golden Path" as their only conceivable escape from this predictive quagmire.

In Ursula K. Le Guin's seminal novel The Left Hand of Darkness, the humanoid inhabitants of the planet Gethen have not only mastered the elusive art of prophecy but routinely produce precise data on past, present, or future events upon request. In this particular narrative, however, this remarkable ability serves primarily as a minor, albeit intriguing, plot device.

Poetry

For the ancients, the concepts of prediction, prophesy, and poetry were often inextricably intertwined. [^38] Prophecies were frequently delivered in verse, and indeed, one of the words for a poet in Latin was "vates," which also carried the connotation of a prophet. [^38] Both poets and prophets, in their respective roles, frequently claimed to be inspired by forces external to themselves, channeling insights from beyond the mundane. In contemporary cultures, theological revelation and poetic expression are typically perceived as distinct, and often even opposing, domains. Yet, despite this modern separation, the two are still frequently understood together, symbiotic in their foundational origins, their ultimate aims, and their inherent purposes. [^39] Some things, it seems, never truly change.

See also

• Expectation – Anticipation that a future event or consequence is likelyPages displaying short descriptions of redirect targets
• Forecasting – Making predictions based on available data
• Futures studies – Study of postulating possible, probable, and preferable futures
• Omen – Portent, harbinger
• Oracle – Provider of prophecies or insights
• Predictability – Degree to which a correct prediction of a system's state can be made
• Prediction market – Platforms for betting on events
• Predictive modelling – Form of modelling that uses statistics to predict outcomes
• Prognosis – Medical term for the likely development of a disease
• Prognostics – Engineering discipline
• Reference class forecasting – Method of predicting the future
• Regression analysis – Set of statistical processes for estimating the relationships among variables
• Thought experiment – Hypothetical situation
• Trend estimation – Statistical technique to aid interpretation of dataPages displaying short descriptions of redirect targets

Footnotes

Further reading

• Ialenti, Vincent (2020). Deep Time Reckoning: How Future Thinking Can Help Earth Now. The MIT Press. ISBN 9780262539265.
• Rescher, Nicholas (1998). Predicting the future: An introduction to the theory of forecasting. State University of New York Press. ISBN 0-7914-3553-9.
• Tetlock, Philip E.; Gardner, Dan (2016). Superforecasting: The Art and Science of Prediction. Crown. ISBN 978-0804136716.

Authority control databases

International

• GND

National

• United States
• Japan
• Czech Republic
• Latvia
• Israel

Other

• Yale LUX

Wikiquote has quotations related to Prediction.

Footnotes

1. "predict". Oxford English Dictionary (Online ed.). Oxford University Press. (Subscription or participating institution membership required.) ↩

2. Silver, Nate (2012). The Signal and the Noise : Why so many predictions fail—but some don't. New York: Penguin Press. ISBN 978-1-59420-411-1. ↩

3. Cox, D. R. (2006). Principles of Statistical Inference. Cambridge University Press. ISBN 978-0-521-68567-2. ↩

4. Siegel, Eric (2013). Predictive Analysis: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken, NJ: John Wiley & Sons. ISBN 978-1-118-35685-2. ↩

5. Julier, S. J.; Uhlmann, J. K. (2004). "Unscented filtering and nonlinear estimation". Proceedings of the IEEE. 92 (3): 401–422. CiteSeerX 10.1.1.136.6539. doi:10.1109/jproc.2003.823141. S2CID 9614092. ↩

6. Fox, John (2016). Applied Regression Analysis and Generalized Linear Models (Third ed.). London: Sage. ISBN 978-1-4522-0566-3. ↩

7. Rudin, Cynthia (2019). "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead". Nature Machine Intelligence. 1: 206–215. doi:10.1038/s42256-019-0048-x. PMC 9122117. ↩

8. Hendry, Andrew P (2023). "Prediction in ecology and evolution". BioScience. 73 (11): 785–799. doi:10.1093/biosci/biad083. ↩

9. Garcia Hernandez, Maria Inmaculada (2018). "Life time prediction for low energy and ecological effects bituminous mixtures". Construction and Building Materials. 118: 108–113. doi:10.1016/j.conbuildmat.2017.09.158. S2CID 139437088. ↩

10. Califf, R.M. (2018), "Biomarker definitions and their applications", Exp Biol Med (Maywood), 243 (3): 213–221, doi:10.1177/1535370217750088, PMC 5813875, PMID 29405771 ↩

11. Woodley, S.M.; Catlow, R. (2008), "Crystal structure prediction from first principles", Nat Mater, 7 (12): 937–946, Bibcode:2008NatMa...7..937W, doi:10.1038/nmat2321, PMID 19029928 ↩

12. "Predictive medicine: Genes indicate diseases before symptoms do". Archived from the original on 2010-12-27. Retrieved 2009-02-24. ↩

13. Borry P; Evers-Kiebooms G; Cornel MC; Clarke A; et al. (June 2009). "Genetic testing in asymptomatic minors: background considerations towards ESHG Recommendations". Eur. J. Hum. Genet. 17 (6): 711–9. doi:10.1038/ejhg.2009.25. PMC 2947094. PMID 19277061. ↩

14. "What is the prognosis of a genetic condition?". Genetics Home Reference. NIH: U.S. National Library of Medicine. Retrieved 2018-05-20. ↩

15. "Prognosis". Nature Publishing Group. Retrieved 20 May 2018. ↩

16. Hansebout, R. R.; Cornacchi, S. D.; Haines, T.; Goldsmith, C. H. (2009). "How to use an article about prognosis". Canadian Journal of Surgery. Journal Canadien de Chirurgie. 52 (4): 328–336. PMC 2724829. PMID 19680521. ↩ ↩²

17. Gould, Stephen Jay (2013), "The Median Isn't the Message", AMA Journal of Ethics, 15 (1): 77–81, doi:10.1001/virtualmentor.2013.15.1.mnar1-1301, PMID 23356812 ↩

18. McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS (2000). "Users' guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group". JAMA. 284 (1): 79–84. doi:10.1001/jama.284.1.79. PMID 10872017. ↩

19. Friedmann PD, Brett AS, Mayo-Smith MF (1996). "Differences in generalists' and cardiologists' perceptions of cardiovascular risk and the outcomes of preventive therapy in cardiovascular disease". Ann. Intern. Med. 124 (4): 414–21. doi:10.7326/0003-4819-124-4-199602150-00005. PMID 8554250. S2CID 25470460. ↩

20. Gordon, Dan (2005). Beat the Sports Books. New York, United States: Cardoza. ISBN 978-1-5804-2174-4. ↩

21. Burke, Brian (2008). "NFL Win Prediction Methodology". ↩

22. Dare, William H (2006). Risk Return and Gambling Market Efficiency (PDF). Oklahoma City, United States: Oklahoma State University. Archived from the original (PDF) on 2013-07-19. Retrieved 2012-11-05. ↩

23. Constantinou, Anthony; Fenton, N.; Neil, M. (2012). "pi-football: A Bayesian network model for forecasting Association Football match outcomes" (PDF). Knowledge-Based Systems. 36: 322–339. CiteSeerX 10.1.1.420.4110. doi:10.1016/j.knosys.2012.07.008. ↩

24. Constantinou, Anthony; Fenton, N.; Neil, M. (2013). "Profiting from an inefficient Association Football gambling market: Prediction, Risk and Uncertainty using Bayesian networks". Knowledge-Based Systems. 50: 60–86. doi:10.1016/j.knosys.2013.05.008. ↩

25. Patel, Mahesh (24 Jan 2023). "Football prediction". Lootrs. Retrieved 24 Jan 2023. ↩

26. "Soccer picks and predictions". Soccer Punt. ↩

27. Overland, Indra (2019-03-01). "The geopolitics of renewable energy: Debunking four emerging myths". Energy Research & Social Science. 49: 36–40. Bibcode:2019ERSS...49...36O. doi:10.1016/j.erss.2018.10.018. hdl:11250/2579292. ISSN 2214-6296. ↩

28. Peek, P.M. African Divination Systems: Ways of Knowing. page 2. Indiana University Press. 1991. ↩

29. Definition of divination ↩

30. Kanade, Shantanu; Kanade, Anuradha (2023). "Astrological Predictions Using Artificial Intelligence and Machine Learning". Harbin Engineering Journal. 44 (7): 1949–1958. Retrieved 13 September 2025. ↩ ↩²

31. "Astrology is booming, thanks to technology and younger enthusiasts". The Economist. 15 January 2025. Retrieved 13 September 2025. ↩

32. "Is Astrology AI Reliable? Our Astrologer Breaks Down Using ChatGPT to Read Your Birth Chart". Teen Vogue. 24 July 2025. Retrieved 13 September 2025. ↩

33. "Can artificial intelligence and astrology work together?". India Today. 3 September 2025. Retrieved 13 September 2025. ↩

34. "KundliGPT – AI-based Astrology Chatbot". KundliGPT. Retrieved 13 September 2025. ↩

35. Stallings, A. E. (2020-08-22). "Poetry and Prophecy". Poetry Foundation. Retrieved 2020-08-22. ↩

36. Franke, William (2016-05-09). "Poetry, Prophecy, and Theological Revelation". Oxford Research Encyclopedia of Religion. doi:10.1093/acrefore/9780199340378.013.205. ISBN 9780199340378. Retrieved 2020-08-22. ↩