Statistical Forecasting Software

Most demand forecasts are built on a single method chosen by habit. Simple moving average because it is familiar. Exponential smoothing because someone set it up years ago. The method stays even when the data changes. The problem is that no single forecasting method works best for every demand pattern. Stable demand with no trend behaves differently than demand with a clear upward trend. Seasonal products need a completely different approach than items with flat, irregular consumption. Using the wrong method does not just produce a less accurate forecast. It produces systematically biased safety stock levels, reorder points, and procurement timing. The Demand Forecasting Tool runs five methods simultaneously on your historical data: Simple Moving Average, Weighted Moving Average, Single Exponential Smoothing, Holt's Double Exponential Smoothing for trending data, and Holt-Winters Triple Exponential Smoothing for data with both trend and seasonality. For each method, it automatically optimizes the smoothing parameters to minimize error on your specific data rather than using defaults. It then scores all five methods against your history using three error metrics: MAPE, MAD, and MSE. The best-fit method is identified automatically and used to generate the forward forecast. The Safety Stock tab takes the forecast error directly from the best method and calculates safety stock and reorder point across four service level targets using the standard formula. Paste your data, set your lead time and service level, and get a defensible stocking recommendation in under two minutes. Link in the comments. #SupplyChain #DemandForecasting #InventoryManagement #ProcurementAnalytics #CPSM

🚀 𝗧𝗼𝗽 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝟮𝟬𝟮𝟱  Whether you're a quant, data scientist, or machine learning engineer... You’ve probably worked with time series data.  • Forecasting.                           • Signal smoothing.  • Anomaly detection.                    • Feature extraction.   I’ve spent months learning the most practical, scalable, and production-ready libraries for time series problems. Here’s the breakdown (sorted by GitHub ⭐): 📈 1. 𝗣𝗿𝗼𝗽𝗵𝗲𝘁 (𝗠𝗲𝘁𝗮) – 18.4k⭐ Perfect for quick, explainable forecasts with seasonality & holidays. e.g. Predict daily product sales including Black Friday surges. ⚡ 2. 𝗡𝗶𝘅𝘁𝗹𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 – 12k⭐ Includes statsforecast, neuralforecast, and TimeGPT. e.g. Train ARIMA or transformer models at lightning speed. 📊 3.𝗧𝗦𝗳𝗿𝗲𝘀𝗵-8.8⭐ Provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis. 🎯 4. 𝗗𝗮𝗿𝘁𝘀 – 8k⭐ Plug-and-play API for ARIMA, RNNs, and ensembles. e.g. Backtest multiple models on energy usage data. 🧠 5. 𝗦𝗞𝗧𝗶𝗺𝗲 – 7.8k⭐ Like scikit-learn, but for time series. e.g. Pipeline + tune multiple time series regressors. 🔮 6. 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – 4.6k⭐ Great for Temporal Fusion Transformers + interpretable DL. e.g. Forecast multi-variate sequences like inventory + demand. If you're serious about DL in time series, start here. 🎁 𝗕𝗢𝗡𝗨𝗦 𝗟𝗜𝗕𝗥𝗔𝗥𝗜𝗘𝗦 𝘁𝗼 𝗘𝘅𝗽𝗹𝗼𝗿𝗲:  • 𝗽𝗺𝗱𝗮𝗿𝗶𝗺𝗮 – Auto ARIMA, just like R, but in Python.  • 𝘀𝘁𝗮𝘁𝘀𝗺𝗼𝗱𝗲𝗹𝘀 – For traditional statistical modelling and diagnostics.  • 𝗔𝗥𝗖𝗛 – For financial time series & volatility modeling.  • 𝘁𝘀𝗺𝗼𝗼𝘁𝗵𝗶𝗲 – Smoothing and outlier removal in seconds.  • 𝗣𝘆𝗙𝗹𝘂𝘅 – Bayesian and classical methods in one package.  • 𝗾𝘂𝗮𝗻𝗱𝗹-𝗺𝗲𝘁𝗮𝗯𝗮𝘀𝗲 – Build visual dashboards in minutes. 💥 Who is this for?  • Data scientists working on production pipelines  • ML engineers deploying neural models  • Analysts needing fast, explainable forecasts  • This list will save you weeks of trial & error. 👉 Bookmark it. Share it. Test it. ♻️ Repost to share the time series magic, and follow Puneet Khandelwal for more such insights on quant and ML. P.S. What’s YOUR go-to Python library for time series? Drop it below 👇

🔍 Forecasting in IBP vs Kinaxis vs OMP vs Relex vs Blue Yonder In today’s dynamic supply chains, accurate forecasting is essential for agility, efficiency, and resilience. Here’s how the top platforms stack up: Via 📊 SAP IBP • Uses time-series, AI/ML models via the Predictive Analytics Library (PAL) • Integrates forecasting into end-to-end S&OP, inventory, and demand planning • Real-time insights with SAP HANA for large volumes and collaborative planning ⚡ Kinaxis RapidResponse • Enables concurrent planning: forecast changes trigger real-time supply impact • Supports ML, causal forecasting, and demand sensing • Ideal for fast-moving, highly responsive supply chain environments 🏗️ OMP • Strong in multi-echelon and hierarchical forecasting • Forecasting is tightly integrated with finite capacity planning • Suited for complex manufacturing networks needing synchronized demand-supply logic 🛒 Relex Solutions • Designed for retail/FMCG, with store- and SKU-level forecasting • Uses AI/ML for promotions, weather, seasonality, and life-cycle forecasts • Automates replenishment and forecasting with strong daily granularity 🤖 Blue Yonder • Powered by Luminate AI/ML platform with probabilistic forecasting • Great for demand classification, demand sensing, and omni-channel retail • Strength lies in prescriptive recommendations and event-driven planning ✅ Quick Comparison: • IBP → Best for integrated enterprise planning (SAP users) • Kinaxis → Best for agility & real-time scenario planning • OMP → Best for manufacturing complexity and constraint-based planning • Relex → Best for retail-level granularity and automation • Blue Yonder → Best for AI-first, omni-channel retail and supply. 📌 Forecasting isn’t one-size-fits-all. Choosing the right platform depends on your industry, complexity, and decision velocity. Let’s connect if you’re evaluating tools or planning a digital supply chain transformation! #Forecasting #SupplyChainPlanning #SAPIBP #Kinaxis #OMP #Relex #BlueYonder #DemandPlanning #RetailTech #AIinSupplyChain #SOP #SupplyChainTransformation #Concurrengplanning 💬 👇🏻For queries on digital transformation using SAP IBP, Kinaxis, OMP, or Blue Yonder — feel free to reach out or drop a message. Let’s explore how the right tool can accelerate your supply chain journey. 🚀 Stefanini Group Stefanini North America and APAC Stefanini Brasil Stefanini EMEA Sandy Sankara Bala Upadhyayula Easwara Dhananjay Karanam Veerabhadra Rao Kakarapalli satish mallina Santosh Chavan Chaitanya Josyula

Google just open-sourced a time series foundation model. It works with any data without training. Traditional forecasting models need to be trained on your specific dataset before they can predict anything. Google's TimesFM works differently. you give it historical data, and it generates forecasts out of the box. This is possible because they trained the model on 100 billion real-world time-points across domains like traffic, weather, and demand forecasting. Key features: - supports up to 16K context length for deeper historical coverage - built-in probabilistic forecasting with quantile predictions - works with both PyTorch and JAX It currently sits at the top of GIFT-Eval, the standard benchmark for time-series forecasting. If you work with demand forecasting, financial data, or any time-series problem, this is worth exploring. I've shared the link to the GitHub repo in the first comment. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!

Want to level up your forecasting skills: Check out Pythons NeuralProphet! Here is what you need to know about it: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁? NeuralProphet is an open-source time series forecasting Python package that combines the simplicity and interpretability of Facebook’s Prophet package with the advanced capabilities of neural networks utilizing PyTorch. It is designed to handle complex patterns in your data, such as multiple seasonalities, trends, and holidays, while being easy to use and integrate into your existing workflows. 𝗠𝗮𝗶𝗻 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Unlike traditional Prophet, NeuralProphet incorporates neural networks, which allow it to capture more patterns and dependencies in the data.    • 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: It supports daily, weekly, monthly, and custom time frequencies, making it adaptable to various forecasting needs.    • 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁-𝗕𝗮𝘀𝗲𝗱 𝗠𝗼𝗱𝗲𝗹: NeuralProphet models trends, seasonalities, and holidays as distinct components, making the forecasts more interpretable.    • 𝗔𝘂𝘁𝗼-𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗧𝗲𝗿𝗺𝘀: The inclusion of auto-regressive terms improves the model’s ability to predict future values based on past observations. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗡𝗲𝘂𝗿𝗮𝗹𝗣𝗿𝗼𝗽𝗵𝗲𝘁? 1. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: By integrating neural networks, NeuralProphet can capture complex patterns and seasonality that traditional methods might miss. This leads to more accurate and reliable forecasts.     2. 𝗨𝘀𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: NeuralProphet matches the simplicity of Prophet, making it accessible even if you’re not a deep learning expert. Its intuitive interface allows you to set up and run forecasts quickly.     3. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Despite its advanced modeling capabilities, NeuralProphet maintains the interpretability of its components, helping you understand the underlying factors driving your forecasts.     4. 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Whether you’re dealing with daily sales data, monthly revenue, or weekly web traffic, NeuralProphet’s flexible framework can be tailored to meet your specific needs.     5. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: NeuralProphet can handle large datasets and multiple seasonalities, making it suitable for complex forecasting tasks in dynamic environments. Use the power of NeuralProphet to level up your forecasting game and deliver insights that drive business success. What tools are you using or plan to use for building forecasts? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanlaytics #datascience #neuralprophet #python #forecasting

Google quietly built and open-sourced a powerful time-series AI model called TimesFM that can analyze massive real-world data and predict future trends across industries. Developed by Google Research, TimesFM is trained on over 100 billion real-world data points and is designed for zero-shot forecasting, meaning it can generate accurate predictions on new data without any additional training or fine-tuning. Unlike traditional forecasting systems that require custom models for each use case, TimesFM works as a general-purpose engine that can detect patterns in time-based data like sales, traffic, markets, or demand, making predictions instantly with minimal setup. The model is available publicly on GitHub under an Apache 2.0 license, making it accessible for developers and businesses to run locally and build forecasting systems without relying on expensive infrastructure or proprietary tools. This shows where AI is heading next, not just generating text or images, but helping predict what happens next, and becoming a core layer behind real-world decision making across industries.

Time series forecasting is one of the most common ML problems faced in industry. It’s easy to get started, but finding the right model isn't easy. What models should you use and when? 👇 1️⃣ When you’ve got few data points per time series or need long term forecasts: use statistical models like ARIMA / SARIMA / SARIMAX or even prophet. My tip: try AutoARIMA from the statsforecast library built by Nixtla. It’s great, optimised and super easy to use! 2️⃣ When you’ve got a larger dataset, use boosting algorithms like XGBoost or LightGBM since those algorithms often perform quite well. The downside is that you'll need to spend more time engineering features (i.e lags). 3️⃣ What about deep learning? Most of the time, stats or boosting algorithms will do the job. I would only use deep learning algorithms when there is a real value in it: 👉 When rows within your dataset can be represented as a graph (for example if you need to predict traffic speed within a road network where the speed in one road can depend on adjacent roads): use graph convolutional networks or variants of it. 👉 If you need to take images into account (for example predicting the weather based on satellite images): use convolutional neural networks or variants of it to extract useful features from images. 4️⃣ And Foundation Models? This space is starting to take off although it is still very early stage. You can test TimeGPT built by Nixtla, TimesFM built by Google or Chronos from Amazon. In my opinion, those models aren't yet as good as the traditional ones but the space is progressing very rapidly! Do you agree? What models do you use for forecasting? Let me know in the comments! PS: Graph below taken from the skforecast documentation, another great library for time series forecasting.

Moirai, Salesforce’s new time series language model, is a fast and fun forecasting tool. Moirai uses a Masked Encoder-based Transformer trained on 27 billion observations. I tried it with hourly energy demand from ERCOT. The model is pretrained so we don't have to train it. Instead, we give it a context window which it uses to make a prediction. I pushed the model to give hourly forecasts for the month of January. You can see the model struggle with large swings in demand. Basically, the model's context window is causing a lag in the same way that a moving average forecast exhibits a lag. In another project, I used this dataset with the IBM Granite models and it performs better but requires a lot more work. Moirai produced a forecast about every second. Granite took 30 seconds per forecast. With Granite, you could really see the improvement in accuracy by increasing the context window - but with more context comes more processing time. And that may be the key part of using this new class of pretrained model. we'll see. https://lnkd.in/gGdGR4Q4 #timeseries #Forecasting #python #datascience

“𝘐𝘵'𝘴 𝘵𝘰𝘶𝘨𝘩 𝘵𝘰 𝘮𝘢𝘬𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘰𝘯𝘴, 𝘦𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦.” - Yogi Berra Business teams often need quick forecasts of metrics to plan resource allocation and set goals. While these forecasts don't need to be perfectly accurate, they should provide reasonable approximations of expected outcomes. Though forecasting is a rich and complex field, sometimes what you need most is interpretability and flexibility to account for various one-off events and still get a reasonable enough forecast. The amusingly named forecasting library - 'Prophet' is a popular tool for generating such forecasts. Built at Facebook and released as an open source library, Prophet is widely used for forecasting business data. It's an additive linear model that breaks down time series into long-term trends, seasonality, and special events. The model quickly learns trends and seasonality from historical data, while analysts must provide domain expertise about special events that effect past trends. I've used Prophet to set team goals where stakeholder buy-in was crucial. The model's interpretability and flexibility proved particularly valuable in these situations. Typically, in a year, there are a few one-off unexpected events that had a business impact but that we do not expect to see next year again. Prophet allows for handling such events easily, and being able to do so builds stakeholder confidence in the forecast. Being able to quickly decompose the forecast into its components is also helpful in interpreting the results. While Prophet isn't the most accurate model available - and more advanced models should be considered when precision is paramount - it remains a valuable tool for creating reasonable business forecasts. Key requirements and limitations when using Prophet: ▪️ 𝐃𝐚𝐭𝐚 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬: Prophet works best with data that has regular timestamps (daily, weekly, monthly) and performs poorly with irregular time intervals ▪️ 𝐇𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐝𝐚𝐭𝐚 𝐧𝐞𝐞𝐝𝐬: Requires at least one full season of historical data to capture seasonality patterns effectively ▪️ 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Assumes that historical patterns will continue into the future, which may not hold during major market shifts or disruptions, though this is something most forecasting models assume ▪️ 𝐂𝐡𝐚𝐧𝐠𝐞 𝐩𝐨𝐢𝐧𝐭 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: May struggle with abrupt changes in trends unless explicitly specified through changepoints 💡 𝐏𝐫𝐨 𝐭𝐢𝐩: Always validate Prophet's forecasts against simpler models (like moving averages) and use cross-validation to assess forecast accuracy before implementation (𝘓𝘪𝘯𝘬𝘴 𝘵𝘰 𝘗𝘳𝘰𝘱𝘩𝘦𝘵 𝘸𝘦𝘣𝘱𝘢𝘨𝘦 𝘢𝘯𝘥 𝘱𝘢𝘱𝘦𝘳 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴. 𝘍𝘪𝘨𝘶𝘳𝘦 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘧𝘳𝘰𝘮 𝘗𝘳𝘰𝘱𝘩𝘦𝘵'𝘴 𝘸𝘦𝘣𝘱𝘢𝘨𝘦)