Analyzing Current Conditions for Accurate Forecasts

If you're in Central-Southern Europe, you may have noticed some unusual behavior in your weather app recently. One day, you're expecting a hot week with temperatures well above 30°C, but the next day, the forecast suddenly shifts to a typical autumn week with temperatures between 15-20°C and rain. What's happening? The issue lies in the models struggling to predict a phenomenon known as a "cut-off low." This is a small, isolated pocket of cold air that is expected to move towards Central Europe in the coming days. A slight shift of just a few kilometers in this area can lead to significant differences in the forecast. To better understand the uncertainty, take a look at this animation, which illustrates the behavior of the geopotential height at 500 hPa (essentially, the pressure field at higher altitudes) over the next few days according to ensemble models. Each line represents a specific scenario (this particular model has 50 different "members"), while the colors indicate different values of the variable (geopotential height). At the beginning of the animation, the lines are closely clustered, indicating low spread and high confidence. However, as the cut-off low descends towards Europe, its final position becomes highly uncertain. Within just a few hours, the spread between the ensemble members increases so much that making an accurate forecast becomes nearly impossible. Looking at the last frame, would you confidently offer a client an accurate forecast a week in advance? This is the current state of the art in weather modeling, and no AI can rescue us from this challenge, yet :) #ensemble #forecasting #cutofflow #ecmwf

Forecasting is a critical aspect of data science, with various methodologies for achieving accurate predictions from a technical perspective (the science part). However, successfully implementing forecasting in practice requires blending expert opinions and ensuring the results make sense (the art part). In this blog, data scientists from Meta share their approach to balancing both the art and science of forecasting. The team explains this balance through two key aspects. The first aspect is the validation of the forecast, which involves assessing the quality of a forecast. Various error metrics are available, and the team emphasizes the importance of selecting the right ones based on business needs. Additionally, to conduct backtesting validation, the team notes that creating a representative test of the forecast often requires human judgment, such as removing volatile or non-representative periods (e.g., the spread of the pandemic in 2020 as a unique period). The second aspect is the incorporation of product impact. Forecasting is usually not just based on historical performance but also on expected upcoming product changes. When product decisions (e.g., launching a new user experience) are made, their potential impact can be integrated into the forecasts to provide more accurate predictions. Since there are different confidence levels in the product change’s impact, the inclusion criteria can be based on associated confidence levels: for example, if a product change can be measured through an experimental holdout, it can be added to forecasting with higher confidence. This layered approach allows the team to adjust the artistic component to blend expected product impacts into future projections appropriately. Forecasting involves many nuances, from selecting the right models and metrics to incorporating expert judgment and product impacts. This tech blog serves as a valuable reference for understanding these complexities and offers insights into effectively combining the art and science of forecasting. #datascience #analytics #forecasting #prediction #art #science – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/g6yx9zkC

Forecasting in Banking: Managing Uncertain Economic Environments Forecasting in the realm of banking is far from a straightforward process. Although the ultimate objective is to arrive at the most plausible predictions possible, the ever-changing economic landscape often presents challenges that make absolute precision impossible. However, that does not mean financial institutions should shy away from attempting to create reliable forecasts. When making forecasts, it is crucial to base these predictions on prudent and conservative assumptions. Banks often rely on historical data to project future trends; although this method has its merits, especially in stable economic conditions, it may not be the most advantageous approach when the economy is in flux. It is essential to factor in the realistic possibility of economic changes, such as interest rate fluctuations or market volatility, to arrive at more robust forecasts. Scenario analysis serves as an invaluable tool for generating realistic expectations about future financial conditions. It allows treasury professionals to examine various outcomes, assessing each for its likelihood and potential impact on the bank’s finances. Scenario analysis provides the advantage of preparedness, offering a range of plausible outcomes rather than fixating on a single, ideal projection. Modern technology, e.g. data analytics and algorithms, can offer increasingly sophisticated ways to improve the accuracy of forecasting models. While technology can significantly aid in making more accurate projections, it's crucial to remember that these tools should complement, not replace, human expertise. A balanced approach, incorporating both technological solutions and skilled professional judgement, tends to yield the most beneficial results. Regulatory frameworks often require banks to maintain a certain level of forecasting accuracy to ensure stability and to protect the interests of stakeholders. Consequently, a bank should always be aware of these requirements and incorporate them into their forecasting methodologies. Regulatory compliance, although often time consuming, provides an additional layer of scrutiny that helps to improve the forecasting process. It is important to understand that forecasting is not a one-off activity. Economic conditions change, sometimes in unpredictable ways, necessitating a revisit of previous forecasts. A best practice is to schedule regular review periods where assumptions can be reassessed, and forecasts updated, to reflect the most current and accurate information available. Overall, the approach to forecasting in uncertainty should be one of cautious optimism. The goal is not necessarily to predict the future with any accuracy, but to understand a range of plausible scenarios and prepare accordingly. By doing so, banks can make more informed decisions, better manage risks, and contribute to the long-term stability and success of their financial institutions.

I was interviewing a bright candidate for a demand planner role a while back. To gauge his practical thinking, I posed a scenario. "Imagine your system generates a baseline forecast of 10,000 units for a key product next month. The statistical model is sound. What's your next action?" He gave a textbook-perfect answer about reviewing historical trends, model accuracy, and checking for outliers. All crucial steps. I paused. "That's an excellent start. But what if that 10,000, as precise as it looks, is missing the most critical piece of information?" He seemed curious, waiting for the answer. This is a scenario I see play out in many organizations. We invest heavily in sophisticated forecasting systems that are brilliant at analyzing the past. But they often lack forward looking context. A forecast is just a number until we enrich it. Many industry studies highlight that forecasts relying purely on historical data can often miss the mark significantly, sometimes by as much as 30-40% for more volatile items. The plan becomes a mathematical exercise, disconnected from the commercial realities on the ground. This is where the concept of Demand Enrichment becomes invaluable. It is the structured process of layering qualitative intelligence on top of the quantitative baseline. In a previous role, we transformed our forecast accuracy not by buying new software, but by changing our process. Our statistically generated forecast was our starting point, not our destination. We built a simple but disciplined enrichment framework: - Collaborative Input: We worked with the sales team to capture insights on key account promotions, new listings, or potential risks. This wasn't a casual chat; it was a structured input into the plan. - Marketing Integration: The marketing team’s activity calendar was overlaid onto the demand plan. We could now quantify the expected uplift from a specific campaign instead of just hoping for the best. - Market Intelligence: We dedicated a small part of our demand review meeting to discussing competitor activity and market trends, translating these discussions into tangible assumptions in our plan. Suddenly, the number had a narrative. 10,000 units was no longer just a point on a graph. It became "10,000 units, which is composed of 8,500 baseline sales, an anticipated lift of 2,000 units from the 'Summer Sale' campaign, offset by a potential 500 unit loss due to a competitor launching a similar product." This enriched number is something the entire organization can understand, align on, and execute against. It transforms the forecast from a passive prediction into an active planning tool. ----- If you have any questions about Demand and Supply planning, feel free to ask using the links in the Bio. P.S. How does your organization go beyond the numbers to tell the full story of your demand?

"Geopolitics, Geoeconomics and Risk: A Machine Learning Approach" In today's complex world, what truly drives a country's financial risk? Is it domestic policy, geopolitical flare-ups, or the pulse of global markets? I'm excited to share our new paper from BBVA Research, "Geopolitics, Geoeconomics and Risk: A Machine Learning Approach," co-authored with Tomasa Rodrigo. We developed a novel high-frequency dataset for 42 countries and used a suite of machine learning models to forecast sovereign risk with greater accuracy. Here are three key takeaways for professionals in finance, economics, and risk management: 1. Global Financial Conditions Reign Supreme 👑 Our analysis confirms the overwhelming importance of the "Global Financial Cycle." U.S. monetary policy (proxied by the 2-year Treasury yield) and global financial volatility (the VIX) are the two most dominant drivers of sovereign risk, far outweighing other factors. This underscores that global "push" factors set the baseline for risk appetite everywhere. 2. The Predictive Power of News is Non-Linear 📰 ➡️ 📈 Simply adding news-based indicators (like Geopolitical Risk or Policy Uncertainty) to traditional linear models yields only modest gains. The real breakthrough comes from using non-linear models like Random Forests, which saw forecast accuracy improve by over 20%. This proves that the value of news lies in its complex, non-linear interaction with financial conditions—something simpler models miss entirely. 3. Geopolitical Shocks are Amplifiers, Not Primary Drivers 🔊 Our scenario analysis revealed a critical dynamic: geopolitical and policy uncertainty shocks, while important, often have a manageable impact in isolation. However, their effect becomes powerfully amplified when combined with a high-volatility environment and tightening global financial conditions. This highlights the state-dependent nature of risk, where the global financial backdrop determines whether a geopolitical spark fizzles out or ignites a fire. For policymakers and investors, these findings highlight that effective risk management requires not only monitoring domestic fundamentals but also understanding the powerful, often non-linear, interplay between news-based sentiment and the global financial cycle. You can read the full paper to explore the detailed methodology, case studies (including the Russia-Ukraine war and U.S. trade policy), and network analysis. Full Paper Link at Arxiv : https://lnkd.in/dM-M8V_D Full Paper Link at BBVA: https://lnkd.in/d7wZvkZA I'd love to hear your thoughts in the comments. #Geopolitics #Geoeconomics #Finance #Economics #MachineLearning #AI #RiskManagement #DataScience #BBVAResearch #SovereignRisk

Google DeepMind and Google Research have developed a new experimental AI model to predict tropical cyclones, and the results on recent hurricanes like Hurricane Erin are really exciting. Watch Olivia Graham from our team explain it below. 🌀Tropical cyclones cause immense destruction and seriously impact communities. Improving the accuracy and timeliness of our forecasts is critical for protecting property and saving lives. 💧Traditional models💧 Physics-based models struggle to accurately predict both a cyclone's path and its intensity. This is because a cyclone's path is influenced by vast atmospheric currents, while its intensity depends on complex turbulent processes within and around its core. ✨Our new model✨ Our new experimental model is a single system that overcomes the traditional tradeoff between track and intensity. It's trained on two distinct types of data 1. A vast reanalysis dataset that reconstructs global weather patterns from millions of observations. 2. A specialized database containing key information about the track, intensity, size, and wind radii of nearly 5,000 observed cyclones from the past 45 years. This allows the model to learn from historical events in a way that traditional models cannot. We’re working with the National Hurricane Center to test this experimental model out this season.  It was really gratifying to see this writeup from the former chief of the hurricane specialist unit there on Hurricane Erin, the strongest Atlantic storm this year. According to his analysis, our model (GDMI in the graphs) had the most accurate forecasts for both track and intensity for the first 72 hours, outperforming a number of the best physics-based models and even the consensus models used by forecasters. This model is now live on Weather Lab, where it's generating 50 possible scenarios for potential future outcomes. If you'd like to explore the model yourself, check it out on Weather Lab: https://lnkd.in/gY9z5wCK Analysis on Hurricane Erin from the former chief of the hurricane specialist unit at the National Hurricane Center: https://lnkd.in/gTHHXdup

Scientists Combine Climate Models For More Accurate Projections -- https://lnkd.in/ga-82Kaw <-- shared technical article -- https://lnkd.in/gHFTDAYj <-- shared paper -- Researchers... have created a new method for statistically analyzing climate models that projects future conditions with more fidelity. The method provides a way to adjust for models with high temperature sensitivities—a known problem in the community. By assigning different weights to models and combining them, the researchers estimate that the global temperature will increase between 2 and 5° Celsius by the end of the century. This projection, published in Nature Communications Earth & Environment [link above], aligns with previous projections, although this novel framework is more inclusive, avoiding the rejection of models that was common practice in previous methods... A key parameter for these models—known as equilibrium climate sensitivity or ECS—describes the relationship between change in carbon dioxide and corresponding warming. Although the Earth system has a true ECS, it is not a measurable quantity. Different lines of evidence can provide a plausible picture of the Earth's true ECS, which can alleviate the uncertainty of simulation models. However, many models assume a high ECS and predict higher temperatures in response to more atmospheric carbon dioxide than occurs in the real Earth system. Because these models provide estimates about future conditions to scientists and policymakers, it is important to ensure that they represent the conditions of the Earth as faithfully as possible. Previous methods mitigated this issue by eliminating models with a high ECS value. "That was a heavy-handed approach," said Massoud. "The models that were thrown out might have good information that we need, especially for understanding the extreme ends of things." "Instead, we adopted a tool called Bayesian Model Averaging, which is a way to combine models with varying influence when estimating their distribution," said Massoud. "We used this to constrain the ECS on these models, which enabled us to project future conditions without the 'hot model problem.'"... This new method provides a framework for how to best understand a collection of climate models. The model weights included in this research informed the Fifth National Climate Assessment, a report released on Nov. 14 that gauges the impacts of climate change in the United States. This project also supports the Earth System Grid Federation, an international collaboration led in the U.S. by DOE that manages and provides access to climate models and observed data…” #GIS #spatial #mapping #climatechange #spatialanalysis #spatiotemporal #model #modeling #numericmodeling #global #statistics #weighting #bayesian #modelaverging #climatesensivity #climatemodels #projection #ECS #earthsystem #ORNL

FP&A teams say they track forecast accuracy. Very few do it consistently, dynamically, and at scale. Usually it’s a static table. One month. One snapshot. No context for trends or bias. That’s a problem—because forecast accuracy only matters over time. This is where rolling dynamic arrays change the game. Instead of manually rebuilding accuracy tables each month, you can use functions like FILTER, TAKE, DROP, SEQUENCE, and LET to create a rolling comparison between Forecast and Actuals that automatically expands as new periods are added. Here’s the practical setup: ◽ Store Forecasts and Actuals in structured tables ◽ Use FILTER to align periods that exist in both datasets ◽ Apply TAKE or DROP to define a rolling window (last 3, 6, or 12 months) ◽ Calculate variance, % variance, or absolute error dynamically ◽ Let the array spill—no copy-paste, no broken ranges The result is a live accuracy engine that updates the moment a new actual hits. Why this matters in FP&A: ◽ You see directional bias, not just point-in-time misses ◽ Rolling windows prevent one-off anomalies from distorting performance ◽ Leaders get trend-based insight instead of static variance noise ◽ Your accuracy KPIs stay intact during reforecasts and plan refreshes In short: you stop reporting accuracy and start managing it. Question for you: If you looked at your last 6 rolling months right now, would your forecast bias be obvious—or hidden? If you’re building rolling forecast models, accuracy dashboards, or executive-ready Excel systems like this, that’s exactly the type of FP&A work I help teams design and scale on LinkedIn.

Understanding Time Series Analysis: A Practical Guide for Finance Professionals Every day, we deal with data that changes over time—stock prices, sales figures, economic indicators, even weather patterns. But how do we make sense of all this movement? That's where Time Series Analysis comes in. I've just completed a comprehensive guide that breaks down time series analysis into simple, practical terms. Here's what you need to know: What is Time Series Analysis? Simply put, it's a technique to understand patterns in data collected over time. Think of it like reading the story in your data—what's the overall trend? Are there seasonal patterns? What about unexpected surprises? The Four Main Components: 1. Trend – The overall direction. Is your business growing, shrinking, or staying flat over months or years? 2. Seasonality – Regular patterns that repeat. Like retail sales jumping in December every year, or electricity demand peaking in summer. 3. Cycles – Long-term wave patterns tied to economic conditions. Think boom and bust cycles that last years, not months. 4. Irregular Changes – The unexpected stuff. Market crashes, policy changes, natural disasters—things you can't predict but need to account for. Why Should You Care? If you work in finance, business, or any field dealing with data over time, understanding these patterns helps you: Forecast better – Predict what's coming next Make smarter decisions – Know what's normal vs. what's unusual Manage risk – Anticipate changes before they happen Plan strategically – Understand the real drivers of your business From Theory to Practice: This isn't just academic stuff. Banks use it for credit risk. Retailers use it for inventory planning. Energy companies use it for demand forecasting. The tools work across every industry. The Bottom Line: Whether you're predicting stock prices, planning your company's budget, or managing supply chains, time series analysis is the skill that turns raw numbers into actionable insights. I've created a detailed guide covering: How to break down any time series Real-world applications in finance Different forecasting methods (from simple to advanced) How to choose the right approach for your data Practical examples you can use immediately If you work with data, trends, or forecasting, this guide is for you. Whether you're an MBA student, financial professional, or just curious about how data tells stories, there's something here for you. Have you used time series analysis in your work? I'd love to hear your experiences in the comments below. Best Wishes, V. Ravichandran #TimeSeries #DataAnalysis #Finance #Forecasting #DataScience #QuantitativeFinance #BusinessAnalytics #MBA #CFA #Financial Modelling #RiskManagement #Analytics

Part 1 of our 3-part Strategic Forecasting Series is live. Historical time tracking data is one of the most underutilized tools in financial planning. Most teams treat it as administrative, not strategic. In Part 1, we show how to: • Extract 24–36 months of time data to reveal patterns • Build seasonal labor models for more accurate forecasts • Link labor and revenue projections • Create buffer plans for variability Real example: a client stayed within 2% of their forecast  and secured board approval for a major purchase without friction. Forecasting isn’t guesswork. It’s a strategic advantage when built on real data. Ready to make your numbers actionable? Start leveraging your time data to drive smarter decisions today.