We all know that a product’s price influences demand. Yet there are still retailers who regard pricing and forecasting as two separate activi
The price elasticity of basic necessities such as milk, eggs and bread is low. Consumers will always continue to buy these.
Industry insiders explained during the FOS Asset Class event why market making is not the same as the “house” in traditional sports betting.
The permission gap is the distance between the part of a system that sees a local problem and the part allowed to fix it. More data does nothing to close it.
In the industry, waiting for a town to grow is called land banking.
That land isn’t waiting for a decision to build. It’s waiting for a decision about what kind of store to build.
Zillow slightly upgrades its national home price outlook—predicting that over the next 12 months, U.S. home prices are likely to shift +0.0%.
Discover how Fastly uses the Gini coefficient, a metric usually reserved for economics, to model edge traffic inequality, improve cache efficiency, and plan capacity.
Major events change the shape of the traffic we serve.
traffic inequality -> cache behavior -> CPU utilization -> headroom
In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.
We all know they’re casinos. It’s time to look at the data behind the froth.
The vast majority of these bets are not on questions that might produce useful information. Roughly 90% of Kalshi’s trading volume (dollars exchanging hands between bettors) is from sports betting, making Kalshi effectively a sports gambling website with a small prediction market attached. I find…
I’ve found that the benefits of prediction markets fall into five different categories.
Prerequisite: basic familiarity with what a prediction market is …
Printable pdf. It’s been a few years since I wrote a broad post on energy, so I’m providing an update in one easy to read place. More detailed specific posts on energy are here. If you want t…
Creating society-destabilizing incentives for fun and profit!
All prediction market platforms trade continuously, which is the same mechanism the stock market uses. Buy and sell limit orders can be posted at any…
A hands-on tutorial with Python and Darts for demand forecasting, showcasing the power of TiDE and TFT
Prediction markets are legal, contrary to popular belief. But they remain unpopular, because they lack key features that make markets attractive.
Over the past two decades, Google has hosted two different internal platforms for predictions. Why did the first one fail — and will the other endure?
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Gleangen
Hey guys, welcome back to my R-tips newsletter. Supply chain management is essential in making sure that your company’s business runs smoothly. One of the key elements is managing inventory efficiently. Today, I’m going to show you how to estimate inve...
This article will take you through some of the best Business Forecasting project ideas you should try. Business Forecasting Project Ideas.
In this article, I'll take you through the task of Demand Forecasting and Inventory Optimization using Python.
The answer to a simple question of why do consumer products manufacturers not use retail data in the management of the supply chain?
Graph-based models capture correlations efficiently enough to enable machine learning at scale.
3rd edition
Learn how to build MMMs for different countries the right way
2nd edition
1 post published by TheZvi on July 26, 2018
Epistemic Status: Sadly Not Yet Subsidized Robin Hanson linked to my previous post on prediction markets with the following note: I did briefly mention subsidization, as an option for satisfying th…
Contests that non-contestants consume for entertainment are a fixture of economic, cultural and political life. We exploit injury-induced changes to teams’ line
There are many ways of estimating how long a software project will take. All of them are a waste of time.
The story of a decade-plus long journey toward a unified forecasting model.
Framework improves efficiency, accuracy of applications that search for a handful of solutions in a huge space of candidates.
Applications from cancer to covid-19
A leading global retailer has invested heavily in becoming one of the most competitive technology companies around. Accurate and timely demand forecasting for millions of item-by-store combinations is…
A series of posts on the limits of human forecasting, and what we may learn from the very best research into the topic.
Results from the BIN paper, or why reducing noise in your decision making is far easier than fighting your cognitive biases.
This report describes 12 common flaws, errors, and misadventures that occur in people’s heads when predictions are made.
Update, Dec 12, 2016: There is a follow up post discussing the outcome of all of this after the election results were known.