Data-Driven Forecasting

📈 𝗔 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗶𝗻 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗧𝗶𝗺𝗲𝘀𝗙𝗠 In finance, forecasting isn’t optional; it’s 𝘀𝘂𝗿𝘃𝗶𝘃𝗮𝗹. Whether it’s predicting trading, credit risk, or demand planning, predicting the future makes or breaks outcomes. Simple, transparent models still rule in regulated areas. But in high-stakes, unregulated spaces, accuracy is everything, and that’s where cutting-edge models take over. 𝗘𝗻𝘁𝗲𝗿 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗧𝗶𝗺𝗲𝘀𝗙𝗠 🚀 Google Research has introduced 𝗧𝗶𝗺𝗲𝘀𝗙𝗠, a foundation model for time series forecasting, trained on 𝟭𝟬𝟬 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 real-world data points. 𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗶𝘁 𝗮𝘀 𝗚𝗣𝗧 𝗳𝗼𝗿 𝘁𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀. 🔹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:  • Decoder-only transformer (like LLMs).  • Instead of words, it uses patches of time-points as tokens.  • Capable of flexible context (input) and horizon (forecast) lengths. 🔹 𝗜𝗻𝗽𝘂𝘁𝘀 & 𝗢𝘂𝘁𝗽𝘂𝘁𝘀:  • Input: raw time series patches/textual data.  • Output: future sequences — and it can generate longer forecasts in fewer steps (reducing error accumulation). 🔹 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:  • Zero-shot forecasts (no retraining needed) that outperform ARIMA, ETS and even rival deep learning models like DeepAR & PatchTST.  • Evaluated on domains like retail, weather, traffic, and finance. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗲?  • 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 & 𝗔𝘀𝘀𝗲𝘁 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: Faster forecasts, no lengthy retraining.  • 𝗥𝗶𝘀𝗸 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: Long-horizon macro + credit simulations with more nuance.  • 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Blending text (news, filings, sentiment) with historical series for richer signals. ✅ Out-of-the-box accuracy across domains. ✅ Foundation models aren’t just for language anymore; they’re coming for finance.  • 𝗟𝗶𝗻𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gc3PhT-S  • 𝗚𝗶𝘁𝗵𝘂𝗯: https://lnkd.in/gVUPJh9t  • 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: https://lnkd.in/gZ-VksQ6 💬 Do you see multimodal foundation models like TimesFM reshaping forecasting in finance? 🔁 Repost to spread the word. 📌 Follow Puneet Khandelwal for more on quant, ML, and data science breakthroughs. #Finance #MachineLearning #DataScience #Forecasting #TimeSeries #Google #AI #Quant #Trading #ARIMA

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.

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

Your financial forecast is lying to you. (Save this + Repost for others if it's useful ♻️) It's not your fault. It's your method. After leading FP&A teams for over a decade, I see the same mistake kill budgets again and again: Relying on a single source of truth. The secret isn't finding one 𝘱𝘦𝘳𝘧𝘦𝘤𝘵 technique. It's combining the right ones. Here's my go-to "accuracy booster" combo: 1. 𝗗𝗿𝗶𝘃𝗲𝗿-𝗕𝗮𝘀𝗲𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴    You estimate the impact of major planned business changes.        ✅ 𝗧𝗵𝗲 𝗚𝗼𝗼𝗱:    It accounts for real-world strategy (new products, market expansion, etc).        ❌ 𝗧𝗵𝗲 𝗕𝗮𝗱:    It can be heavily influenced by human bias. (Hello, happy ears).     2. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴    You use historical data and algorithms to project trends.        ✅ 𝗧𝗵𝗲 𝗚𝗼𝗼𝗱:    It's pure data. Completely immune to internal politics or bias.        ❌ 𝗧𝗵𝗲 𝗕𝗮𝗱:    It can overreact to recent blips in data and miss the bigger picture.     See the problem? Each one has a blind spot. My solution is brutally simple: Run both methods in parallel. Then take the average of the two. This simple act balances human insight with unbiased data. The result? A forecast you can actually trust. It's how we consistently beat targets. What's the biggest forecasting challenge you face? Let's talk about it in the comments. 👇 -Christian Wattig P.S. This isn't just theory. I've implemented this exact blended approach at several high-growth companies. It just works.

Machine Learning-Powered Demand Sensing: Revolutionizing Real-Time Decision Making In the realm of demand forecasting, machine learning (ML) is reshaping the landscape by enabling real-time analysis for predicting short-term demand with exceptional precision. Unlike conventional methods that rely solely on historical data, ML-driven demand sensing incorporates a wide array of data sources, including sales figures, inventory levels, weather patterns, social media trends, and economic indicators, to swiftly identify fluctuations in demand. For instance, in the context of event management, demand sensing proves invaluable in anticipating attendance variations influenced by external factors such as weather conditions or concurrent events. Through sophisticated ML algorithms, subtle trends like a sudden spike in ticket purchases triggered by social media engagements can be detected, empowering organizers to promptly adjust their strategies related to inventory, staffing, or promotions. This innovative approach not only slashes forecast errors by as much as 50% but also streamlines resource distribution and mitigates risks associated with overbooking or inventory shortages. By translating raw data into actionable intelligence, demand sensing fosters agility and accuracy in navigating dynamic market conditions.

𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗺𝗮𝗿𝘁 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻𝘀 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Retailers bleed profit from poor inventory accuracy, overstocking slow movers while running out of trending items. Manual forecasting can’t keep pace with changing demand, promotions, or seasonality. The result? Dead stock, markdown losses, and frustrated customers. In the era of instant commerce, inventory agility is revenue protection. Without intelligent forecasting, retailers risk losing both sales and trust. 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 AI-powered forecasting models analyze sales trends, customer demand, weather data, and even social media signals to predict what products will sell, where, and when. Smart systems auto-adjust procurement and replenishment, ensuring shelves stay stocked but not overloaded. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 📦 50% fewer stockouts, improving customer satisfaction 💰 20% reduction in excess inventory holding costs ⚙️ 30% faster inventory turnover and replenishment cycles 📊 Predictive insights improving vendor coordination and planning 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 When supply chains think ahead, businesses no longer chase demand, they meet it before it arrives. AI creates agility, ensuring the right product is always in the right place at the right time. https://lnkd.in/ea2dYXJc

🚀 New Publication Alert! I want to share our latest paper, “A data-driven and context-aware approach for demand forecasting in the beverage industry,” co-authored with my marvelous collaborators: Benedict Jun Ma, Maggie Zhaoxia Huang, Sebastián Villegas, and Jaime Macias, now published in the International Journal of Logistics Research and Applications. In this work, we develop and test a context-aware forecasting framework that integrates both endogenous data and exogenous factors such as holidays and temperature. By classifying SKUs based on demand volume, volatility, and intermittency, we identify four distinct demand types—each requiring tailored forecasting models. Our findings demonstrate how statistical, machine learning, and deep learning models perform across these demand clusters, providing practical guidance for supply chain managers seeking to enhance forecast accuracy and responsiveness. 📄 Read the full paper here: https://lnkd.in/e6xNapt3 #DemandForecasting #MachineLearning #SupplyChain #AI #Logistics #BeverageIndustry #Forecasting

🚀A good forecast isn’t just helpful, it’s everything. In my recent project, I built an interactive dashboard to track and forecast sales across global customer segments and product lines. What started as rows of raw numbers turned into a clear picture of how the business is doing, and where it's headed. 📌 With features like: Cumulative sales vs forecast tracking Profit breakdown by customer segments and countries Product growth insights Weekly sales trends Discount band analysis I was able to help highlight what’s working, what’s not, and what actions to take next. One key insight? The Enterprise segment showed a significant downturn in profit, which may have gone unnoticed without comparing it to forecasted goals. 💡 This reminded me that good forecasting isn't just about planning, it's about staying ahead, spotting red flags early, and giving decision-makers the confidence to act. Built using: Power Query, DAX and Power BI If you're working with data and not using it to see forward, you might only be getting half the story. #datafam

I am excited to share our new article: A data-driven approach to predicting power outages during winter storms in the southern U.S. leveraging nonparametric machine learning models In February 2021, Winter Storm Uri severely impacted much of the southern United States, triggering unprecedented large-scale power outages. Recognizing that a similar extreme weather event could occur in the future, this study identifies as its primary research objective the development of a baseline power outage prediction model specifically tailored for the southern region of the United States. Central to this objective is the research question: Which variables and which regression models play the most significant role in accurately predicting power outages in this context? Given that large-scale outages are, in essence, a direct result of imbalances between electricity supply and demand, population was considered a key influencing factor. Furthermore, to ensure the model adequately reflects the meteorological characteristics of winter storms, several atmospheric variables—such as dew point and atmospheric pressure—were incorporated into the analysis. These variables are intended to capture the environmental dynamics that underpin outage occurrence during extreme cold events. Four machine learning models—Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were employed in this study. In addition, to enable a comparison between these four machine learning approaches and traditional statistical models, Ridge regression and Lasso regression were also implemented, utilizing population and geographic information data in conjunction with meteorological variables to achieve this objective. Lee, J., Zhang, Z., & Paal, S. G. (2025). A data-driven approach to predicting power outages during winter storms in the southern US leveraging nonparametric machine learning models. Computational Urban Science, 5(1), 62. https://lnkd.in/gZQMDRrY