KarpelesPublishing
UserCompanion for The Art and Science of Forecasting: 27 runnable chapter notebooks, AI skills for every chapter, the Complete Forecasting Skill, and the reconcile-tdbu launch desk model. No book text.
Categories
Indexed Skills (30)
all-chapters-forecasting
Use when applying the forecasting book across multiple methods, reproducing its chapter notebooks, or forecasting a collection of time series with one instruction.
forecast-workflow
Use whenever someone needs a forecast, a probability or a launch estimate and is not studying a specific chapter. Seven gates, each producing an artifact the next command requires, so a person with no forecasting experience and an expert follow the same auditable path from question to scored result.
forecasting-ch01-markets
Use when market prices or trading signals need a persistence benchmark and information-timing audit, or studying forecasting book chapter 1.
forecasting-ch02-belief
Use when binary success evidence must update a base rate or a selected sample needs a survivorship audit, or studying forecasting book chapter 2.
forecasting-ch03-weather
Use when seasonal structure or sensitivity to initial conditions needs to be understood before choosing a forecast, or studying forecasting book chapter 3.
forecasting-ch04-smoothing
Use when regular demand, workload or revenue series whose recent level, trend and repeating seasonality may forecast the next periods, or studying forecasting book chapter 4.
forecasting-ch05-state-space
Use when a noisy measurement must track a latent changing level online, or studying forecasting book chapter 5.
forecasting-ch06-arima
Use when a regular series has lag dependence that may support ARIMA or seasonal ARIMA, or studying forecasting book chapter 6.
forecasting-ch07-monte-carlo
Use when uncertain inputs must be propagated through a calculation or simulation precision needs auditing, or studying forecasting book chapter 7.
forecasting-ch08-delphi
Use when structured anonymous expert rounds are needed for sparse-evidence quantity forecasts, or studying forecasting book chapter 8.
forecasting-ch09-expert-scoring
Use when probability forecasters need fair scoring and calibration diagnostics on common resolved events, or studying forecasting book chapter 9.
forecasting-ch10-superforecasting
Use when a resolvable event needs an explicit probability, evidence-based updates and a forecast journal, or studying forecasting book chapter 10.
forecasting-ch11-crowds
Use when several independent quantity estimates need aggregation with attention to common bias and outliers, or studying forecasting book chapter 11.
forecasting-ch12-benchmarking
Use when forecast methods need a fair chronological benchmark across series, origins and horizons, or studying forecasting book chapter 12.
forecasting-ch13-retail-ml
Use when related retail series and known calendar, price or promotion features may support a pooled tree forecast, or studying forecasting book chapter 13.
forecasting-ch14-global-neural
Use when many related short histories may benefit from a global probabilistic neural model, or studying forecasting book chapter 14.
forecasting-ch15-foundation-models
Use when a pretrained time-series model is being evaluated on a new series under a fixed model revision, or studying forecasting book chapter 15.
forecasting-ch16-prophet
Use when a business series has defensible calendar effects and changing trend suitable for Prophet, or studying forecasting book chapter 16.
forecasting-ch17-probabilistic
Use when forecast intervals or quantiles need scoring, calibration and decision interpretation, or studying forecasting book chapter 17.
forecasting-ch18-hierarchy
Use when forecasts across stores, regions or products must add to consistent totals, or studying forecasting book chapter 18.
forecasting-ch19-diffusion
Use when first adoption of a durable product or category may follow diffusion with an uncertain ceiling, or studying forecasting book chapter 19.
forecasting-ch20-marketing-mix
Use when media carryover, response saturation or attribution stability must be assessed against business sales, or studying forecasting book chapter 20.
forecasting-ch21-supply-chain
Use when inventory decisions, bullwhip amplification or intermittent demand need forecast-policy evaluation, or studying forecasting book chapter 21.
forecasting-ch22-causal
Use when an intervention effect needs a defended comparison rather than a purely predictive forecast, or studying forecasting book chapter 22.
forecasting-ch23-epidemics
Use when reporting delays distort recent counts or a compartment model is being used for conditional epidemic scenarios, or studying forecasting book chapter 23.
forecasting-ch24-structural-breaks
Use when forecast residuals may signal a structural break and adaptation policies need explicit evaluation, or studying forecasting book chapter 24.
forecasting-ch25-reference-classes
Use when project duration or cost needs an outside-view reference class and a risk-based commitment, or studying forecasting book chapter 25.
forecasting-ch26-decision-readiness
Use when a probability forecast must guide a concrete action under explicit costs and ongoing evaluation, or studying forecasting book chapter 26.
forecasting-ch27-directed-forecasting
Use when a business question needs routing from available evidence to a defensible forecast, especially proxy-based launch volumes and trial/repeat timing, or studying forecasting book chapter 27.
reconcile-tdbu
Interviews the user, then builds and runs a top-down/bottom-up new-product volume forecast that reconciles a trial-and-repeat model against a market-share model. Use this whenever someone wants to forecast first-year volume, units, or share for a new product or concept; mentions trial and repeat, repeat rate, share of choice, category penetration, MPCT (a monadic product concept test, the survey a concept is shown to one respondent group at a time), depth of repeat, or a volumetric concept test; asks to reconcile two forecasts or two modelling approaches that disagree; asks why a bottom-up estimate does not match a top-down one; or brings up the top-down bottom-up triangulation model from *The Art and Science of Forecasting* (chapters 20 and 27). Also use it for teaching or writing about reconciliation between estimation procedures, balancing variables, or survey overstatement correction. Trigger it even when the user has only partial data, the skill is built to interview for the missing inputs rather than re
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.