Science Forecasting Models

New research models the likelihoods of different climate scenarios. It shows that 3°C isn’t a worst-case. It’s the most likely. Up until now, climate scenarios have been treated as narrative pathways without assigned probabilities. Climate scientists have resisted giving scenarios a likelihood because of deep uncertainty. That is, the full range of outcomes due to physical, social, political and technological changes can't be known, and therefore, probabilities cannot be reliably estimated. Climate scenarios were described as exploratory tools, not forecasts and were designed to illuminate plausible pathways, not predict them. But... intuitively, we know that some climate futures are more likely than others. This information is helpful for business decision-making. This new paper from the EDHEC Climate Institute challenges the idea that probabilities can't be assigned to climate scenarios and provides two robust, data-driven methods to do it. The first is an 'informative method', which starts with economists’ views on the social cost of carbon (SCC). In effect, it converts wishful thinking into plausible expectations. The second is a 'maximum entropy method'. It makes as few assumptions as possible, using current carbon prices and basic policy constraints as the only inputs. What’s remarkable is that both approaches produce results that are very similar. Does this mean that some climate pathways are more locked in than we think? Model outputs: 🔸 The most likely temperature anomaly in 2100 is between 2.8–3.0ºC 🔸 There is a 35–40% chance of exceeding 3.0ºC 🔸 There is just a 1% chance of staying below 1.5ºC The model was also tested using Oxford Economics scenarios. The results were even more shocking. 🔸 The ‘Climate Catastrophe’ carries a likelihood of 57.5%. 🔸 The ‘Climate Distress’ scenario carries a likelihood of 35% 🔸 Together, they make up 92.5% of the total These high temperatures increase the likelihood of triggering irreversible tipping points, for which standard damage functions no longer apply. This is dangerous territory. 𝗠𝘆 𝗧𝗮𝗸𝗲 Most companies use climate scenarios that treat all futures as exploratory scenarios. But this doesn't allocate future risk efficiently. Without probabilities, we cannot optimise capital allocation between mitigation (transition risk) and adaptation (physical risk). Assigning probabilities to scenarios changes the conversation. It equips firms to weigh investment in risk reduction not just by severity but also by likelihood. Personally, I believe this is a critical next step in climate risk planning. Assigned likelihoods should be accompanied by uncertainty bounds—so decision-makers can assess not just what’s likely, but how confident we can be in those estimates. Source: https://lnkd.in/exy5TDS8 _____________ 𝘍𝘰𝘭𝘭𝘰𝘸 𝘮𝘦 𝘰𝘯 𝘓𝘪𝘯𝘬𝘦𝘥𝘐𝘯: Scott Kelly

AI is completely rewriting the rules of weather forecasting, and this video from NVIDIA is a perfect example of how fast things are moving. In just under 5 minutes, the video demonstrates Earth-2, a platform that allows you to run global weather forecasts in mere seconds using just a few lines of Python. You can seamlessly switch between data sources (like ERA5, GFS, IFS) and even swap out entire AI models (like FourCastNet, GraphCast, or Aurora) with a single line of code. But NVIDIA isn’t alone. We are witnessing an arms race among big tech to solve weather prediction: - Google DeepMind has GraphCast and NeuralGCM, which have already outperformed gold-standard physical models in many metrics. - Microsoft released Aurora, a foundation model trained on over a million hours of data, claiming to be 5000x faster than traditional numerical systems. - IBM & NASA recently open-sourced Prithvi, a "geospatial foundation model" designed not just for weather, but to be fine-tuned for specific climate applications. - Huawei has Pangu-Weather, which famously predicted the path of a typhoon more accurately than traditional methods. Why is this happening? - Compute: Traditional Numerical Weather Prediction (NWP) solves complex physics equations requiring massive supercomputers. AI models, once trained, infer results in seconds on a few GPUs. - Ensemble Forecasting: Because they are so cheap to run, we can generate thousands of scenarios (ensembles) instead of just a few. This is a game changer for predicting low probability extreme weather events. - Data Fusion: These models are proving incredibly good at learning patterns from historical data that pure physics equations might miss. For the geospatial practice, this is a big change. Weather is moving from a static dataset we download to a dynamic capability we run. You no longer need a supercomputer to generate high-resolution forecasts; you just need a GPU and a Python script. We may soon see fine-tuned weather models for specific geospatial use cases like hyper local wind for drones, precise precip for agriculture, or cloud cover for satellite tasking. The latency between data in and forecast out is shrinking to near zero, enabling true real time geospatial intelligence. Have you tried any of these models? What are your thoughts? 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

Hot off our recent transformer paper, we're excited to share another AI model for precision medicine! Biological data collected from patients has exploded in recent years, presenting a challenge: how do we decipher that data to understand which patients will benefit most from specific therapies?  We in the Applied Data Science team at AstraZeneca are thrilled to share our paper in Cancer Cell called "AI-Driven Predictive Biomarker Discovery with Contrastive Learning to Improve Clinical Trial Outcomes." Here, we introduce the *Predictive Biomarker Modeling Framework (PBMF)*, a neural network-powered contrastive learning process that: 🔍 Explores vast multimodal datasets to uncover predictive biomarkers in an automated, systematic, and unbiased manner  🧠 Distinguishes predictive biomarkers (which indicate a likely benefit from a specific therapy) from prognostic biomarkers (which indicate general disease outlook)  💡 Distills its outputs into an interpretable decision tree, showing what drives treatment response In our studies, the PBMF:  📊 Surpassed existing methods in finding predictive biomarkers for immunotherapy success across various cancers in clinical trial and real-world data  📈 Discovered a predictive biomarker in an early-stage trial that boosted efficacy by 15% when retrospectively applied to the corresponding phase 3 clinical trial  📈 Discovered predictive biomarkers in single-arm early phase trial data with synthetic control arms, retrospectively improving the efficacy of the corresponding phase 3 trials by at least 10% We believe the PBMF has the potential to improve the way we design clinical trials and match patients to the right therapies. It can integrate with other models like our Clinical Transformer, creating exciting possibilities to someday discover biomarkers of adverse events, dosing strategies, and even to back-translate new drug targets. Read the full paper here: https://lnkd.in/eveAnVRY   Thanks to all the co-authors: Gustavo Arango, Damian Bikiel, Gerald Sun, Elly Kipkogei, Kaitlin Smith, Sebastian Carrasco Pro, Elizabeth Choe #PrecisionMedicine #ClinicalTrials #AIinHealthcare #Biomarkers #Immunotherapy

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

From Siloed Projections to System-Wide Planning: How We Built Singapore’s Healthcare Capacity Framework 3 years ago, our healthcare demand projections were done in silos. Today, we have a coherent, system-wide framework that links demand to infrastructure, manpower, and budget planning. Honoured by the recognition on the work done by the team. Here’s the transformation journey. The Challenge We Faced Demand for each care setting is projected independently, using different assumptions and methodologies. 2023: Building the Foundation Introduced more granular inputs: added parameters e.g. functional impairment levels and family support in long-term care projections. Linked patient flows: Connected across settings (e.g. ED visits to acute inpatient to community hospital). 2024: Achieving System Coherence The coordination challenge: Working across 8+ divisions (IPP, HSD, PCC, APO, MP&S, HF) while handling new policy simulations & evolving capacity decisions. The solution: Set up Capacity Planning Committee (CPC) as single decision platform, replacing piecemeal EXCO discussions. The breakthrough: Obtained approval for our projections alignment framework: • Single baseline model across all projections • Common parameters where models intersect • Systematic accounting for care transformation impacts Real impact: Secured approval for new hospital beds through white space activation and new hospital sites. 2025: Advanced System Modelling Healthier SG simulation: Collaborated with Duke-NUS to quantify HSG’s long-term impact on healthcare demand and costs - answering our persistent questions. Disease-based projections: Piloted new method for mental health services, endorsed and used for service planning Tight deadline delivery: Completed baseline and care transformation projections across all settings that should have taken a few years to complete within one year. The Framework That Changed Everything Our Long-Term Capacity Planning Framework now seamlessly connects: • Demand drivers (population aging, functional impairment) • Care settings (from acute to community to home-based care) • Resource planning (manpower, infrastructure, budget) Policy interventions like HSG, right-siting efforts, and palliative care strategies are incorporated. Key Lessons Learned 1. Coordination is as important as methodology - The CPC structure solved more problems than technical improvements alone 2. Resilience matters - When our HSG model wasn’t endorsed initially, we went back to fundamentals and rebuilt stakeholder confidence 3. Granular parameters drive better insights - Moving from broad assumptions to specific factors like family support levels improved accuracy The result? A coherent planning system that helps Singapore prepare for demographic transitions while optimising resource allocation across the entire healthcare continuum. What challenges are you facing in system-wide planning and coordination across multiple stakeholders?

𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗼𝘂𝘁𝗹𝗶𝗻𝗲𝗱 𝟲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗣&𝗔 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗯𝗲𝘁𝘁𝗲𝗿 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴. Most finance teams know them. Few actually implement them consistently. Why? Because doing it right has always been painfully manual. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝗸 𝗺𝗲: AI is changing this. Fast. The six practices McKinsey recommends are now achievable at scale: • 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆-𝘄𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 – AI can run hundreds of scenarios and assign P values automatically, not just the three you had time to build manually. • 𝗧𝗿𝘂𝗲 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝗰𝗮𝘀𝗲𝘀 – AI separates baseline trends from management initiatives without the spreadsheet gymnastics. • 𝗕𝗲𝗮𝗿 𝗰𝗮𝘀𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – AI identifies downside risks and models them before you're blindsided. • ��𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗺𝗮𝗰𝗿𝗼 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 – AI flags when one business unit uses different GDP assumptions than another. • 𝗗𝗶𝘀𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲𝗱 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻 – AI tracks the specific components that actually affect your business, not just CPI averages. • 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗯𝗮𝗰𝗸 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 – AI compares forecasts to actuals weekly and learns from variances automatically. 𝗧𝗵𝗲 𝗯𝗿𝘂𝘁𝗮𝗹 𝘁𝗿𝘂𝘁𝗵: Human bias has always been the weak link in forecasting. Optimism creeps in. Assumptions go unchallenged. P-values are applied inconsistently across business units. AI doesn't have a political agenda. It doesn't inflate projections to look good in front of the board. It just processes data. The result? Faster forecasts. More accurate projections. And decisions based on reality, not hope. 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲? 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝗮𝗰𝗸 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 Use AI to compare your forecasts over the last 12 months with actuals. Find where bias lives in your models. 𝟮. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Stop building three scenarios manually. Let AI generate probability-weighted ranges based on actual data patterns. 𝟯. 𝗘𝗻𝗳𝗼𝗿𝗰𝗲 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 Use AI to flag when macro assumptions differ across business units. Inconsistency kills forecast accuracy. Because here's what separates finance teams that drive decisions from those that just report numbers: They use AI to remove bias and deliver forecasts that leadership can actually trust. 𝗦𝗼 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: Which of these six practices is your biggest gap right now? ---------- 🧑💼 I'm a partner at Business Partnering Institute 🤝 We help increase the influence of your finance team 🔔 To see more content, hit the bell on my profile 📘 Order our new book now: https://bit.ly/4h2P9AA 🧑🎓 Enroll in our LinkedIn course: https://bit.ly/4a5fB9l 📻 #FinanceMaster podcast: https://bit.ly/3NLSt73 📺 Follow us on YouTube: https://bit.ly/4bSBut6 📢 Join our WhatsApp channel: https://bit.ly/3WWGOrc 📄 Check out all our templates and cheat sheets here: https://lnkd.in/eC_zuCU4

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

Traditional surrogate-based design optimization (SBDO) is hitting a wall, especially with high-dimensional, complex designs. In this new paper, Dr. Namwoo Kang presents a next-gen framework using generative AI, integrating three key models: - Generative model (design synthesis) - Predictive model (performance estimation) - Optimization model (iterative or generative) Rather than optimizing directly in a high-dimensional design space (x), the workflow introduces a low-dimensional latent space (z) learned via generative models. ➡️ z → x → y z = latent variables x = CAD geometry y = performance (drag, stress, etc.) This means we’re no longer hand-coding design parameters or doing trial-and-error with simplified surrogate models. 🧠 Why this matters: - Parametric modeling is no longer a bottleneck - Complex shapes are learned directly from CAD - Dynamic and multimodal performance data (1D, 2D, 3D) can be used - Near real-time optimization is possible #AI #GenerativeDesign #CAE #DesignOptimization

There is a massive archive of flash flood data hidden inside the news. 📰 Unlike earthquakes, which are tracked by a global network of sensors, the world is living in a “data desert” when it comes to smaller, fast-moving events like flash floods. This data gap is a major hurdle for climate resilience. Without a historical baseline, it’s nearly impossible to predict future hazards at a local level. To solve this, my colleagues at Google Research are introducing a new approach called Groundsource. It uses Gemini to turn 25 years of news articles, government reports, and local bulletins into a historical flash flooding map. The team created a massive global archive of 2.6 million flood events across 150 countries. Using this data, they can provide near-global urban flash flood forecasts up to 24 hours before an event. These forecasts are now being rolled out in Google’s Flood Hub, our free and public platform that provides AI-driven flood forecasts to help communities stay safe. For me, the most exciting part is seeing how AI can transform the world’s “unstructured memory” into a robust scientific baseline. Manually extracting this information at scale would be impossible, but using AI to organize relevant historical data lets us build a more resilient future where communities everywhere can better prepare for flash floods. Learn more about how we’re turning the news into life-saving data. 👇 goo.gle/4ryKfzH