The hottest Forecasting Substack posts right now

And their main takeaways
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Ensemble Weather Forecast API

Open-Meteo • 351 implied HN points • 05 Jun 23
🕹 Technology Weather API Forecasting Models Documentation
  1. Ensemble weather forecasts show a range of possibilities, helping to understand the uncertainty in predictions.
  2. Weather forecasts differ in reliability based on location and weather patterns, affecting the level of uncertainty in predictions.
  3. The Ensemble API combines various weather models, providing access to different weather variables for various purposes.

Advancing Beyond Short-Term Solar Forecasts [Pt.2]

Space Ambition • 79 implied HN points • 08 Dec 23
🔬 Science Astrophysics Forecasting Machine Learning
  1. It's important to understand the solar cycle better and predict solar storms. These storms can cause big financial losses and affect many technologies we rely on.
  2. Currently, we can only accurately predict space weather for about three days ahead. This is because solar events happen quickly, and predicting them is really complicated.
  3. We need more advanced tools and methods, like machine learning, to improve our predictions. Using new technology can help us learn more about the Sun and its effects on Earth.
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March Madness Forecasts: FiveThirtyEight vs. Betting Markets

Mike’s Blog • 78 implied HN points • 07 Apr 23
💰 Finance Data Analysis Forecasting Academic Research Sports
  1. Betting markets slightly outperformed FiveThirtyEight in predicting NBA, NFL, and MLB games.
  2. New data collected for March Madness shows both FiveThirtyEight and betting markets performed similarly, and neither significantly outperformed.
  3. Hypothesis: Both betting markets and experts may have worse accuracy in playoffs and tournaments compared to regular season games.

Superforecasting the premises in “Is power-seeking AI an existential risk?”

Joe Carlsmith's Substack • 58 implied HN points • 18 Oct 23
🕹 Technology AI Forecasting Existential Risk AGI
  1. Good Judgment solicited reviews and forecasts from superforecasters on the argument for AI risk.
  2. Superforecasters placed higher probabilities on some AI risk premises and lower on others compared to the original report.
  3. Author is skeptical of heavy updates based solely on superforecaster numbers and emphasizes the importance of object-level arguments.

What people are not seeing about TimeGPT

Three Data Point Thursday • 19 implied HN points • 16 Nov 23
🕹 Technology AI Data Analysis Machine Learning Forecasting Model development
  1. Time series models, like TimeGPT, are advancing and will provide a significant boost in machine learning capabilities.
  2. Adding time as a feature in models can enhance data analysis due to the information richness of recent data.
  3. Although skepticism exists around time series machine learning models, advancements in generic models like TimeGPT are removing some barriers.

Don't Run Coibion-Gorodnichenko Regressions with Micro Data

inexactscience • 39 implied HN points • 27 Mar 23
💰 Finance Economics Data Analysis Statistics Research Methods Forecasting
  1. Running Coibion-Gorodnichenko regressions with individual data can lead to misleading results. It's important to use appropriate data types to avoid confusion in the findings.
  2. Individual forecasts tend to produce negative results compared to positive results in average forecasts. This means that the insights from these regressions can differ significantly based on the data used.
  3. The methodology is sensitive to noise and measurement errors, which can skew results. Researchers need to be cautious and robust in their approach to ensure accurate interpretations.

Stars are treated differently

Klement on Investing • 1 implied HN point • 17 Dec 24
💰 Finance Investing Markets Forecasting Performance
  1. Star analysts have a bigger influence than regular analysts because their reports can move markets. They are rewarded for giving high-quality forecasts that help investors succeed.
  2. To become a star, analysts need to make good predictions, but staying a star often means making bold and risky calls instead of focusing on quality.
  3. Once an analyst becomes a star, they are judged less harshly for mistakes. This means they can take more risks and make big headlines, even if it doesn't always lead to good outcomes.

Leviathan wakes

Gradient Ascendant • 16 implied HN points • 21 Feb 24
🕹 Technology AI Forecasting Generative AI Models Artificial Intelligence
  1. The author quit their job to work on a new AI-related project motivated by the transformative potential of modern AI technology.
  2. Google's Gemini 1.5 model is a significant advancement in AI capabilities, able to handle an impressive 10 million tokens for input, marking a major leap forward in AI development.
  3. Despite its imperfections, Gemini 1.5 and other advanced AI models are drastically reducing limitations and opening up new possibilities for future technological innovations.

Forecast Scoring Methods

Abstraction • 19 implied HN points • 07 Nov 23
🔬 Science Forecasting
  1. Market-Based Platforms are good for gauging market sentiment, not individual forecasting skill
  2. Reputation-Based Platforms focus on individual performance metrics to identify top forecasters
  3. Consider the ramifications of overconfidence when selecting a scoring system for forecasting

Scoring Rules

Abstraction • 19 implied HN points • 26 Sep 23
🔬 Science Forecasting
  1. Proper scoring rules encourage honest and accurate forecasting by penalizing dishonesty and over/under-confidence.
  2. Improper scoring rules do not incentivize forecasters to report their true beliefs, leading to suboptimal forecasting incentives.
  3. In practice, proper scoring mechanisms like Brier scoring help distinguish skill from noise over multiple rounds and promote honest, calibrated forecasting.

The Insufficient Weirdness Hypothesis

Gradient Ascendant • 20 implied HN points • 01 Jun 23
🔬 Science Future AI Science fiction Artificial Intelligence Forecasting
  1. The future is consistently weirder than expected because of unknown unknowns and unusual juxtapositions.
  2. AI development and outcomes are expected to be highly weird and unpredictable, not following a smooth exponential path.
  3. Weird and unexpected scenarios are more indicative of potential future risks to consider rather than conventional outcomes.

Wanna run 50,000 election sims in 2 seconds?

Matt’s Five Points • 19 implied HN points • 04 Nov 22
🇺🇸 U.S. Politics Elections Forecasting Political Science Data Analysis Public Opinion
  1. You can run a quick election simulation by using an Excel sheet. Just change the win probabilities for each state and the sim does the math for you in about 2 seconds.
  2. Basic election modeling isn't as hard as it sounds. You can easily create your own model with some data and a few calculations to forecast election outcomes.
  3. Strong, accurate models take more work and understanding, but anyone can start trying their hand at it. It can be enjoyable to explore different scenarios with the data.

Results for the Salem/CSPI Prediction Tournament

Center for the Study of Partisanship and Ideology • 11 implied HN points • 29 Aug 23
💰 Finance Forecasting Data Analysis Political Ideology
  1. The winners of the Salem/CSPI Prediction Tournament were announced, including a $25,000 prize and a fellowship
  2. The analysis of the betting markets showed mixed results, with some events being accurately predicted while others were not
  3. Participants in the tournament were mostly young, male, and had a libertarian-leaning political orientation

Chuyện Việt Nam: Forbes vinh danh người dự báo "đến đầu tháng 8/2021 Sài Gòn sẽ hết dịch"

Thái | Hacker | Kỹ sư tin tặc • 19 implied HN points • 21 Feb 22
🏥 Health Politics Public Health COVID-19 Forecasting Vietnam
  1. Ngô Hoàng Anh and team accurately predicted the end of the COVID-19 outbreak in Saigon by August 2021 using their SEIQHCDRO model.
  2. Collaboration with the Epidemiological Modelling Unit ensured adjustments to their model for accurate COVID-19 predictions in Saigon.
  3. Future forecasts by the team suggest a potential new wave of COVID-19 in Saigon from December 2021 to March 2022, depending on the enforcement of preventive measures.

Gradient Flow #39: Becoming TikTok, Next-gen Workflow Orchestration and Forecasting

Gradient Flow • 19 implied HN points • 15 Jul 21
🕹 Technology Data Tools Infrastructure Automation Forecasting
  1. The newsletter discusses next-gen dataflow orchestration and automation systems like Prefect, a startup that helps manage dataflows.
  2. It introduces cool new open source tools like Greykite, a flexible and fast library for time-series forecasting.
  3. BytePlus, a new division of ByteDance, is offering the technology behind TikTok to websites and apps, presenting interesting challenges in the global market.

New $5,000 Prize in the Salem Center/CSPI Forecasting Tournament

Center for the Study of Partisanship and Ideology • 3 implied HN points • 15 Feb 23
🇺🇸 U.S. Politics Forecasting Debt Ceiling
  1. A new $5,000 prize is added to the Salem Center/CSPI Forecasting Tournament to encourage more participation.
  2. The prize will go to the person with the greatest percentage increase in their portfolio value by the end of the tournament.
  3. Various markets on global events and economic forecasts are also discussed in the tournament.

What is in the cards for the Russian oil indstry?

On Energy, Cabbages and Kings • 2 implied HN points • 15 Jul 23
💼 Business Energy Economics Forecasting
  1. The Russian oil industry is facing many challenges and uncertainties for the next decade.
  2. Some predict a collapse similar to what happened in Iran and Venezuela, while others believe past crises have made the industry stronger.
  3. There is complexity and resilience in the Russian oil industry that both insiders and outsiders may be underestimating.

Projecting Solar Production with Weather Data

Solar Powered Data • 0 implied HN points • 09 Jul 23
🔬 Science Solar energy Energy Production Forecasting Data Analysis
  1. The correlation between weather data like solar radiation and solar energy with solar production is high, indicating a predictive relationship.
  2. By using historical and forecasted weather data, it's possible to project solar energy production up to two weeks in advance, offering insights for planning.
  3. Accuracy of solar energy predictions from sources like Visual Crossing is crucial for reliable projected energy production outcomes.

Don't be fooled by Wall Street analysts

Global Markets Investor • 0 implied HN points • 28 Dec 23
💰 Finance Investing Stock Market Forecasting Financial Analysis Economic Trends
  1. Wall Street analysts have consistently missed S&P 500 year-end targets by an average of 15.7% from 2018 to 2023.
  2. It's hard for even the most renowned financial firms to predict exact stock market values, showing the importance of personal research.
  3. Despite sophisticated analysis, Wall Street analysts often get S&P 500 projections wrong, emphasizing the value of independent thinking in investment decisions.

"AI Isn’t Coming for Tech Jobs—Yet" In Asterisk Magazine

Abstraction • 0 implied HN points • 27 Jun 23
🕹 Technology AI Tech Jobs Forecasting
  1. Exploring counterfactual scenarios helps forecast future trends by imagining a world without specific factors like large language models (LLMs).
  2. Using the "outside view" involves making predictions based on broader trends and historical data rather than focusing on specific instances.
  3. Monte Carlo simulations provide an empirically-grounded view by generating random future scenarios based on historical changes, aiding in predicting potential outcomes.

Decoding Expectations

inexactscience • 0 implied HN points • 20 Mar 23
💰 Finance Macroeconomics Behavioral Finance Expectations Economic Models Forecasting
  1. Expectations are key to economic models because they shape how people behave and react to changes in the economy. For example, if people expect prices to rise, they may ask for higher wages.
  2. There is confusion about whether expectations tend to overreact or underreact to information. Evidence shows that expectations can do both—people might overreact to recent events but underreact to larger economic trends.
  3. Bias in expectations is often studied, but noise—random fluctuations and errors—is just as important and can affect forecasts significantly. Understanding both can help improve how we predict economic outcomes.

Are complex models the answer?

Musings on Markets • 0 implied HN points • 01 Dec 10
💼 Business Economics Finance Investment Modeling Forecasting
  1. Complex models can struggle when predicting unpredictable human behavior. Simple models might work better in uncertain situations.
  2. Small changes in a complex model can lead to large unexpected outcomes, a phenomenon known as the butterfly effect.
  3. When faced with uncertainty, it's better to simplify models by focusing on key variables and reducing complexity.

Enrollment Trends

SFEDup • 0 implied HN points • 01 May 23
🚌 Education Market Share Forecasting
  1. Enrollment in SFUSD is down by almost 4,000 students (7.6%) since before the pandemic.
  2. Charter schools overall saw a 5.8% decrease in enrollment, with variations among individual schools.
  3. Cohort survival rates have recovered since the pandemic, but future enrollment projections for SFUSD are challenging due to declining birth rates and migration trends.

Gradient Flow #43: Graph Databases; Language Understanding; Program Synthesis

Gradient Flow • 0 implied HN points • 09 Sep 21
🕹 Technology Data Management Machine Learning Natural Language Processing Deep Learning Forecasting
  1. Graph databases and graph analytics are growing in interest, with use cases and applications expanding.
  2. The NLP Summit offers insights from leading organizations and researchers in the field of Natural Language Processing.
  3. Tools like Darts for time series forecasting and River for online machine learning are open-source libraries enabling easier adoption of advanced machine learning techniques.

Polymath Engineer Weekly #81

Polymath Engineer Weekly • 0 implied HN points • 12 Feb 24
🕹 Technology Programming Forecasting Software Automation
  1. Consider the reasons behind choosing programming languages like Go over Rust, and how they impact problem-solving strategies.
  2. Explore innovative approaches like decoder-only foundation models for time-series forecasting to achieve high performance with less complexity.
  3. Reflect on the impact of intentional choices on software development, understanding tools like TLA+ for formal method and the importance of thoughtful deployment automation.