AI Techniques For Accurate Data Predictions

How Zinnia Used AI to Forecast Daily Call Volumes with 95% Accuracy 📞 At Zinnia, we needed a better way to forecast call center volumes — our existing tool often missed the mark by 10–20%, making staffing plans unreliable So, we rolled up our sleeves and built our own AI forecasting solution: ✅ Combined Prophet (seasonality + trends) with XGBoost (learn from errors) ✅ Used real-world signals like holidays, month-ends, and even Mondays after weekends ✅ Tuned everything with time-aware cross-validation We tested A LOT (even LSTMs and SHAP-based pruning!), but our hybrid model consistently delivered 95%+ accuracy across clients. 🔍 I’ve shared the full breakdown, code, and what worked (and what didn’t) in this Medium article — practical, real-world AI for ops. If you're a data scientist, ML engineer, or even an ops leader — this one’s for you. Josh Everett | Pawan Choudhary | Daniel Gremmell | Eti Gupta #DataScience #Forecasting #AI #XGBoost #Prophet #TimeSeries #MLinProduction #CallCenterAI #WorkforcePlanning #ZinniaTech #AIinOps

What are timeseries LLMs? You are probably familiar with LLMs that generate text, images, or video, but there are also LLMs that generate time series data, which are sets of datapoints at specific time intervals. To use a timeseries LLM you feed it a set of initial (historical) data, and it extrapolates the data (into the future). This is very useful for forecasting tasks, such as predicting web traffic, sales and inventory forecasting, media planning, and even stock prices and weather patterns!  Initial timeseries LLMs could only predict a single series at a time, i.e. univariate forecasting. However, the latest timeseries LLMs such as Chronos2 can also predict multiple co-evolving series (multivariate) and can incorporate factors that influence outcomes (covariates).  For example, using categorical covariates, a forecast of retail inventory can incorporate specific sales events and holidays. Timeseries LLMs use zero-shot learning, which means they immediately make predictions on new data without prior training and tuning. This is possible because the timeseries LLM itself was constructed from large volumes of timeseries data and can immediately extrapolate from new datasets. The resulting predictions will often have higher accuracy that custom-trained models (my own testing has confirmed this). The construction of a timeseries LLM is particularly clever. The process begins with a regular text-based LLM that has already been trained, e.g. an open-weight LLM such as Qwen.  The LLM is then fine-tuned on a large collection of timeseries data that has been quantized and converted into textual sequences. The resulting timeseries LLM is then capable of generating tokens that correspond to a timeseries, which are then dequantized into the new timeseries data. Timeseries LLMs are another great application of new AI techniques to produce results that would previously have required significant effort, e.g., building statistical models. The accuracy of these LLMs is already impressive and improving continually.  I highly recommend evaluating these models for any applications you have involving forecasting of timeseries data. [Note that the above is entirely my own opinion, and in no way represents the views of Amazon]

Combine LLMs and TSFMs: that’s the future of AI for the Industrial Internet. LLMs are great at understanding natural language, summarizing reports, and making complex data accessible. But in industrial environments, that’s not enough. Manufacturing, energy, and IoT systems generate massive streams of time-series data from sensors and machines. To provide predictive maintenance, forecast power generation/consumption, or detect anomalies, we need Time Series Foundation Models (TSFMs). TSFMs specialize in understanding patterns, seasonality, and irregularities in time-series data, making them ideal for predictive maintenance, real-time anomaly detection, and operational optimization. When you bring the two together: . LLMs let people query, interpret, and interact with data naturally. . TSFMs deliver accurate, reliable forecasts and anomaly detection. The result? A complete AI solution that bridges human understanding and machine intelligence—unlocking new levels of efficiency and reliability for industrial customers. The Industrial Internet doesn’t just need AI. It needs LLMs + TSFMs, working together.

𝗨𝗻𝗹𝗼𝗰𝗸 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗗𝗮𝘁𝗮 𝗳𝗼𝗿 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 Industries like manufacturing, finance, and IoT generate massive amounts of time-sensitive data. But how can organizations transform this data into actionable insights and stay ahead of the curve? My latest article dives into 𝘁𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴—a cornerstone of predictive analytics that leverages the time-dependent nature of data to drive smarter, faster decisions. 📈 𝗞𝗲𝘆 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲: ·     Explore how statistical models like ARIMA and machine learning methods enhance time-series forecasting. ·     Discover deep learning techniques, including LSTMs and Transformers, revolutionizing predictive accuracy. ·     Learn real-world applications across industries: o  Predictive maintenance in manufacturing o  Fraud detection and algorithmic trading in finance o  IoT analytics for smarter homes and cities From optimizing supply chains to detecting anomalies in IoT networks, temporal data modeling is reshaping how businesses predict and respond to change. Read the full article to uncover actionable insights and future trends in this transformative field. Let’s unlock the full potential of your temporal data together! #PredictiveAnalytics #TemporalData #AI #IoT #Manufacturing #Finance ------------------------     ✅ Follow me on LinkedIn at https://lnkd.in/gU6M_RtF to stay connected with my latest posts. ✅ Subscribe to my newsletter “𝑫𝒆𝒎𝒚𝒔𝒕𝒊𝒇𝒚 𝑫𝒂𝒕𝒂 𝒂𝒏𝒅 𝑨𝑰” https://lnkd.in/gF4aaZpG to stay connected with my latest articles. ✅ Please 𝐋𝐢𝐤𝐞, Repost, 𝐅𝐨𝐥𝐥𝐨𝐰, 𝐂𝐨𝐦𝐦𝐞𝐧𝐭, 𝐒𝐚𝐯𝐞 if you find this post insightful. ✅ Please click the 🔔icon under my profile for notifications!

Forecasting stands as a formidable challenge within statistics and machine learning, requiring complex data preparation and specialized models that demand profound expertise—often encompassing entire careers. It merges mathematical precision with domain-specific insights, necessitating a fusion of art and science to achieve accurate predictions. We delve into some of the practical challenges of forecasting, especially in the business context, here: https://lnkd.in/ey8mXYBc The advent of large language models prompts the question: Could these models simplify forecasting, reducing its complexity and the need for specialized knowledge? Recent advancements in time series forecasting have highlighted the power of LLMs, marking a transformative shift in the field. Salesforce's MOIRAI model (https://lnkd.in/ex8h2Vt5), with its robust zero-shot forecasting capabilities and adept handling of multivariate data, has shown superior performance over traditional models by offering cross-domain versatility and advanced probabilistic forecasts. Similarly, the LAMP framework (https://lnkd.in/gSiDi9VQ ) integrates LLMs into event prediction, significantly reducing errors and demonstrating LLMs' potential to refine operational processes through a thoughtful analysis of the 'why' behind events. Moreover, TEMPO, a GPT-based model (https://lnkd.in/ebjT3EvM ), has made strides in numerical modeling by decomposing time series into core components and guiding forecasting with prompts, showcasing remarkable accuracy improvements and adaptability to non-stationary data. As the latest innovation in time series forecasting, Chronos by Amazon (https://lnkd.in/eqgFruwU) stands at the forefront. Through a sophisticated process of tokenization, scaling, and quantization, Chronos adeptly transforms numerical sequences into a format readily processed by language models, akin to converting numbers into words. With its focus on the incorporation of cutting-edge data augmentation methods like TSMix and KernelSynth, Chronos represents a significant leap forward. Chronos excelled in performance across two benchmarks, showcasing its forecasting prowess. In familiar scenarios (Benchmark I), it outperformed both traditional and specialized models, especially in handling seasonal data trends. When faced with completely new datasets (Benchmark II), Chronos impressively exceeded traditional methods and matched or surpassed deep learning models. A simple fine-tuning significantly boosted its performance, demonstrating Chronos's adaptability and potential as a versatile tool for accurate forecasting across diverse scenarios. Collectively, these studies underscore LLMs' role in setting new benchmarks for forecasting, characterized by scalability, adaptability, and enhanced precision, revolutionizing traditional, labor-intensive forecasting methods.