Retail Sales Forecasting Methods

Modeling something like time series goes past just throwing features in a model. In the world of time series data, each observation is associated with a specific time point, and part of our goal is to harness the power of temporal dependencies. Enter autoregression and lagging -  concepts that taps into the correlation between current and past observations to make forecasts.  At its core, autoregression involves modeling a time series as a function of its previous values. The current value relies on its historical counterparts. To dive a bit deeper, we use lagged values as features to predict the next data point. For instance, in a simple autoregressive model of order 1 (AR(1)), we predict the current value based on the previous value multiplied by a coefficient. The coefficient determines the impact of the past value on the present one only one time period previous. One popular approach that can be used in conjunction with autoregression is the ARIMA (AutoRegressive Integrated Moving Average) model. ARIMA is a powerful time series forecasting method that incorporates autoregression, differencing, and moving average components. It's particularly effective for data with trends and seasonality. ARIMA can be fine-tuned with parameters like the order of autoregression, differencing, and moving average to achieve accurate predictions. When I was building ARIMAs for econometric time series forecasting, in addition to autoregression where you're lagging the whole model, I was also taught to lag the individual economic variables. If I was building a model for energy consumption of residential homes, the number of housing permits each month would be a relevant variable. Although, if there’s a ton of housing permits given in January, you won’t see the actual effect of that until later when the houses are built and people are actually consuming energy! That variable needed to be lagged by several months. Another innovative strategy to enhance time series forecasting is the use of neural networks, particularly Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are designed to handle sequential data like time series. They can learn complex patterns and long-term dependencies within the data, making them powerful tools for autoregressive forecasting. Neural networks are fed with past time steps as inputs to predict future values effectively. In addition to autoregression in neural networks, I also used lagging there too! When I built an hourly model to forecast electric energy consumption, I actually built 24 individual models, one for each hour, and each hour lagged on the previous one. The energy consumption and weather of the previous hour was very important in predicting what would happen in the next forecasting period. (this model was actually used for determining where they should shift electricity during peak load times). Happy forecasting!

Time Series Residuals Analysis 101 👇🏼 When evaluating a time series forecasting model, residual analysis is one of the most important steps. Residuals—the differences between the actual values and the model’s predictions—help us understand whether the model has captured the underlying patterns or if important structure remains unexplained and what features are missing. In a good model, the residuals should be white noise (no patterns left) and normally distributed (required for model inference and reliable prediction intervals) 🎯 Here’s what each diagnostic plot helps us check: 🔹 Actual vs. Fitted Plot This plot shows how closely the model’s fitted values track the actual observations. It helps you visually spot systematic under- or over-prediction, missed trends, or structural breaks that the model failed to capture. 🔹 Residuals Plot (over time) Plotting residuals across time shows whether they fluctuate randomly around zero. Patterns such as trends, clusters, or seasonal waves indicate that the model has not fully captured the time-dependent structure. 🔹 Residuals ACF (Autocorrelation Function) The ACF plot checks whether residuals are correlated with their own past values. Significant autocorrelation at any lag suggests the model left some temporal structure unmodeled and could be improved. 🔹 Q–Q Plot (Residual Normality Check) The Q–Q plot compares the distribution of residuals to a theoretical normal distribution. Deviations from the diagonal line signal non-normality, which can affect inference and the validity of prediction intervals. 🔹 Residual Density Plot This shows the overall distribution of residuals. A symmetric, bell-shaped curve centered at zero indicates the model errors behave as expected; skewness or heavy tails may highlight model misspecification or outliers. Pro tips: 🔹 Overlay the residual standard deviation on the Actual vs. Fitted plot. I use a range of ±2σ to ±3σ (orange) and bands above ±3σ to immediately spot points where the model’s errors are unusually large, making it easier to diagnose poor fit or outliers. 🔹 Highlight seasonal lags in the residuals ACF. Marking seasonal lag positions (e.g., lag 7, 12, 24, 168—depending on your frequency) in a different color makes it simple to see whether any seasonal structure remains in the residuals, signaling that the model may not have fully captured seasonality. #timeseries #forecasting #datascience

Human forecasters augmented by GenAI improve performance by 23% and vastly outperform AI-only predictions. Fascinating new research has uncovered important lessons, not just on Humans + AI forecasting, but more generally AI-augmented thinking. 🔮Human forecasters provided an LLM with a 'Superforecaster' prompt substantially improved their prediction performance. 📊In contrast to studies in other domains, the improvement was consistent across more and less skilled forecasters. 🔄Even the use of biased models improves performance to a similar degree, showing that the value was in providing additional perspectives to be assessed by human judgment. 💬Back-and-forth interaction is critical to value creation. Simple Humans + AI thinking processes such as incorporating predictions is of limited use. Forecasters using the models through their thinking process is high value. 🌈Prediction diversity is not degraded by use fo LLMs, with users not letting the models homogenize their thinking. 🚀Forecasting is an excellent use case and example for AI-augmented thinking. High-level human decision-making is highly complex and cannot be delegated to machines, but LLMs, used well, can substantially improve outcomes. The 'Superforecaster' prompt used in the study and a link to the pre-print paper are in the post. #foresight #forecasting #humansplusai #augmentedintelligence

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

5 Ways Semiconductor Companies Forecast Demand Despite Long Lead Times and Highly Cyclical Markets 1. Customer Collaboration & Long-Term Supply Agreements (LTSAs) Companies secure 12–36 month forecasts from major customers. Use NCNR (non-cancellable, non-returnable) contracts to lock demand. Example: TSMC receives long-range demand plans from Apple for iPhone SoCs, enabling early wafer allocation. Infineon gets multi-year volume commitments from automotive OEMs for power MOSFETs and MCUs. 2. Multi-Quarter Order Backlog & Pipeline Analysis Continuous analysis of book-to-bill ratios, backlog ageing, and order cancellations. Sharp reductions in bookings often signal a market downcycle. Example: During the 2021 chip shortage, NXP and STMicroelectronics used 6–9 month backlogs to justify increasing wafer starts at foundries. When PC demand crashed in 2022, Intel’s falling book-to-bill warned of overcapacity. 3. Market Intelligence & Macro Indicators Track global semiconductor reports, sector growth, and end-market signals (EVs, cloud, consumer electronics). Example: ON Semiconductor monitors EV adoption forecasts to model future SiC MOSFET needs. Smartphone shipment trends from IDC/Gartner help Qualcomm and MediaTek predict next-year modem and SoC demand. 4. Statistical & Scenario-Based Forecast Models Use historical patterns (seasonality of consumer devices), inventory ratios, and regression models. Run best-case, base-case, and worst-case scenarios. Example: NVIDIA forecasts GPU demand by modeling cloud capex cycles from Amazon, Google, and Microsoft. Memory makers (Samsung, Micron) use scenario models when DRAM/NAND prices swing due to oversupply. 5. Channel Monitoring & Inventory Tracking Track distributor inventory, sell-in vs. sell-through, and sudden stock build-up. A spike in distributor stock often indicates demand softening. Example: Texas Instruments (TI) closely monitors distributor inventory days; rising inventory signals that the industrial market is slowing. Analog Devices (ADI) checks if sensor ICs are stuck in channels instead of reaching OEMs. ~~~~~~ If you are looking to invest in semiconductors and need expert insights, drop us a DM.

Sales forecasting isn’t just about projecting revenue. It’s about understanding what drives revenue. Here are a few examples. (1) Price x Volume I usually don't separate sales into rates and units because of the extensive detail required. Most of my forecasts are all-in sales of price x volume, or rates x units. It's usually 'good enough' and balances accuracy with effort. But you know it's not always appropriate. If you want precision, or scenario modeling, you'll likely need to break these down further. If prices aren't fixed or demand is dynamic, you'll likely need to deliver a more detailed forecast. (2) Include/Exclude Toggles Sales pipelines often contain CRMs with customers at different stages in the sales cycle. Including them, or applying % volume reductions based upon uncertainty, can distort the sales forecast. In my models, I like to include toggles (similar to the checkboxes you see here) that allow for the inclusion/exclusion of sales depending on (a) scenarios, or (b) the stage of the sales process. This lets you easily change your sales forecast without corrupting your formulas. (3) Top-Down Forecasts Not all forecasts can (or should) be bottoms-up. In this example, the company has a huge opportunity with “NFL Confidential” customer. This customer may or may not be landed, which is why there's an include/exclude toggle. FP&A also included macro-level assumptions for the events that will drive sales up or down. It's a top-down estimate, modeled from known business events (the NFL playoffs) from Q4 to Q1. Sales ramp up slightly, then significantly, before they come back down. (4) Customer Concentration This company may be eager to land an NFL team as a customer, as it's both a strategic and financial play. On the strategic side, the company can get greater market exposure. On the financial side, it brings $5.3 million to the top line. But this amounts to 26.5% of total sales, huge concentration. So there are questions to ask: Can the company effectively manage this higher volume? How does this new focus disrupt other operations? Will new roles need to be filled to accommodate the customer? Are different machines and new capex necessary to service the customer? Does the company have the liquidity to obtain raw materials? What timing for deposits and billings allows the company to cash flow? Remember: sales forecasting isn’t just about projecting revenue. It's about understanding the drivers and implications. When sales forecasting becomes a joint effort between sales and FP&A, you get a far more thoughtful planning process.

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

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  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/gWRgTJ2Q 

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Here are 5 machine learning algorithms used for FP&A and #finance time series analysis: ✅ ARIMA/SARIMA: Forecast future revenues and expenses by identifying trends and seasonality. ✅ LSTM: Analyze complex patterns in cash flow or sales data to improve financial planning. ✅ Prophet: Handle unpredictable markets and still make reliable forecasts. ✅ GARCH: Assess and predict market volatility to make more informed investment or budgeting decisions. More detail below ↓ 1. ARIMA (Auto-Regressive Integrated Moving Average) ARIMA helps predict future values by analyzing past data to identify patterns like trends or seasonality. For example, you can use ARIMA to forecast next year’s monthly revenue by recognizing historical trends and seasonal variations, such as higher sales during holiday seasons. 2. LSTM (Long Short-Term Memory) Networks LSTM is an artificial intelligence technique that learns from past data and remembers long-term patterns. It can be used in FP&A to forecast cash flow by identifying recurring inflows and outflows over time, like specific project payments or seasonal cash patterns. 3. SARIMA (Seasonal ARIMA) SARIMA extends ARIMA by incorporating seasonality, making it ideal for forecasting data with regular patterns. For example, you can predict quarterly expenses more accurately if certain quarters have consistently higher costs due to contracts or seasonal demand. 4. Prophet Prophet, developed by Facebook, handles missing data and outliers well, making it useful for complex datasets. To get the code and example for implement it, go here: https://lnkd.in/eJKcHzqU You could use Prophet to forecast annual sales even when your data is incomplete or affected by irregular events like economic shifts. 5. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models volatility and is great for predicting how much financial data varies over time. You can apply it in FP&A to assess and predict the volatility of stock prices in your investment portfolio, helping in risk management and budgeting.