Welcome to Royfactory

Introduction AI Sales Forecasting often starts with feature-based ML (GBDT). This lesson shows when to move to deep learning and how to use foundation models as fast baselines. TL;DR: pick models based on covariate availability, rolling backtests, calibrated uncertainty, and cost/latency. Why it matters: Deep learning only pays off when it reduces decision risk (stockouts/overstock) at an acceptable operational cost. 1) Model landscape (train-from-scratch vs pretrained) Train-from-scratch: DeepAR, TFT, N-HiTS, TiDE, PatchTST Pretrained foundation models: TimesFM, Chronos, TimeGPT Why it matters: Pretrained models accelerate baselining; train-from-scratch can fit your domain more tightly. ...

Introduction AI Sales Forecasting succeeds in production only if you design the operating loop: monitoring → diagnosis → retrain/rollback. Most failures come from broken inputs and silent distribution shifts, not from model math. TL;DR: Monitor (1) data quality, (2) drift/skew, and (3) post-label performance; then release via a registry with canary and rollback. Why it matters: Forecast labels are often delayed. Drift + data-quality monitoring becomes your early warning system. ...

Introduction TL;DR: AI Sales Forecasting must stay consistent across planning levels (total/category/SKU). The common production pattern is (1) generate base forecasts, then (2) apply forecast reconciliation (e.g., MinT) to enforce coherence. For new items, “cold-start” is solved by borrowing signal from hierarchies and similar items (metadata/content/price tiers). Promotions should be designed either as model features or as a separate uplift (counterfactual) estimation pipeline (e.g., CausalImpact/BSTS). Why it matters: Without coherence, different teams will operate on different numbers, breaking replenishment and planning alignment. ...

Introduction TL;DR: AI Sales Forecasting becomes valuable only when it drives ordering decisions. Build a lead-time (or protection-period) demand distribution, pick the right service metric (CSL vs fill rate), and set reorder point/order-up-to levels using quantiles. Avoid “adding daily P95s” to get a lead-time P95—use sample-path aggregation. For reliable uncertainty, calibrate prediction intervals (e.g., conformal forecasting). Why it matters: Forecast accuracy is not the objective; meeting service targets at minimal total cost is. ...

Introduction TL;DR: AI Sales Forecasting with feature-based ML turns time series into a supervised regression problem using lags/rolling stats, calendar signals, and exogenous variables. The winning recipe is: feature taxonomy → point-in-time correctness → rolling-origin backtests → WAPE → quantile forecasts. Why it matters: This approach scales across many SKUs/stores and stays maintainable when your catalog grows. 1) What “feature-based ML” means for sales forecasting Definition, scope, common misconception Definition: convert time series into a feature table (lags/rollings/calendar/exogenous) and fit a regressor (GBDT). Misconception: “GBDT can’t do time series.” It can, if the feature pipeline and validation are correct. Why it matters: Most failures come from leakage and bad validation, not from the model class. ...

Introduction TL;DR: AI Sales Forecasting must be evaluated using genuine forecasts on unseen data, not training residuals. Use rolling forecasting origin (rolling-origin CV) with explicit choices: horizon, step, window type, and refit policy. Report WAPE + MASE (and pinball loss for quantiles) and compare everything against two fixed baselines: seasonal naive + ETS. In this lecture-style part, you’ll build a backtest setup that matches deployment conditions and produces a decision-ready report. ...

Introduction TL;DR: AI Sales Forecasting often fails due to data semantics (schemas, time meaning, leakage), not model choice. Model your sources as sales + calendar + price + promo + inventory/stockouts, then build a stable training/inference view. Enforce point-in-time correctness for time-series feature joins to prevent leakage. Treat stockouts as censored demand and track them explicitly. In this Part 2, you’ll get a practical data model and validation rules you can lift into a warehouse/lakehouse. ...

Introduction TL;DR: AI Sales Forecasting succeeds when forecasts are tied to decisions (inventory, ordering, staffing), not when a model merely outputs numbers. Use an end-to-end flow: requirements → data contract → baselines + backtesting → model strategy → probabilistic forecasts → deployment + monitoring. Prefer probabilistic forecasting (quantiles/intervals) when under- and over-forecasting costs are asymmetric. In this series, AI Sales Forecasting is treated as a production system: dataset design, evaluation, deployment mode, and operational guardrails come first. ...

Introduction TL;DR: Open LLM Leaderboard v2 shifts evaluation toward instruction-following, hard reasoning, long-context multi-step reasoning, and difficult science QA. In the public v2 “contents” view, the Average ranges from 0.74 to ~52.1, and GPQA / MuSR are clear bottlenecks (their maxima are much lower than other tasks). Top entries often include merged/community-tuned models, so you should separate “leaderboard performance” from “production-ready choice.” Why it matters: If you treat a leaderboard rank as a production verdict, you’ll pick the wrong model. ...

Introduction TL;DR: Media summaries put 2026 Big Tech AI infrastructure spending $650B at roughly $650B, while Reuters frames it as more than $630B. (Bloomberg.com) Amazon guided about $200B (company-wide capex), Alphabet guided $175B–$185B, and Meta guided $115B–$135B including finance lease principal payments. (Amazon) The “total” varies mostly because definitions (leases vs cash PP&E) and periods (calendar vs fiscal year) don’t line up perfectly across companies. (Microsoft) Context (first paragraph): 2026 Big Tech AI infrastructure spending $650B is a shorthand for a hyperscaler capex super-cycle aimed at AI data centers, accelerated computing, and networking. Reuters describes the same theme as over $630B combined. (Bloomberg.com) ...