"Prediction Revisited is a ground-breaking book for financial analysts and researchers--as well as data scientists in other disciplines--to reconsider classical statistics and approaches to forming predictions. Czasonis, Kritzman, and Turkington lay out the foundations of their cutting-edge approach to observing information from data. And then characterize patterns between multiple attributes, soon introducing the key concept of relevance. They then show how to use relevance to form predictions, discussing how to measure confidence in predictions by considering the tradeoff between relevance and noise. Prediction Revisited applies this new perspective to evaluate the efficacy of prediction models across many fields and preview the extension of the authors' new statistical approach to machine learning. Along the way they provide colorful biographical sketches of some of the key scientists throughout history who established the theoretical foundation that underpins the authors' notion of relevance--and itsimportance to prediction. In each chapter, material is presented conceptually, leaning heavily on intuition, and highlighting the key takeaways reframe prediction conceptually. They back it up mathematically and introduce an empirical application of the key concepts to understand. (If you are strongly disinclined toward mathematics, you can pass by the math and concentrate only on the prose, which is sufficient to convey the key concepts of this book.) In fact, you can think of this book as two books: one written in the language of poets and one written in the language of mathematics. Some readers may view the book's key insight about relevance skeptically, because it calls into question notions about statistical analysis that are deeply entrenched in beliefs from earlier training. The authors welcome a groundswell of debate and advancement of thought about prediction."--
A thought-provoking and startlingly insightful reworking of the science of prediction
In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance.
The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers:
- Clarifications of commonly accepted but less commonly understood notions of statistics
- Insight into the efficacy of traditional prediction models in a variety of fields
- Colorful biographical sketches of some of the key prediction scientists throughout history
- Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within
With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.
