「forecasting」の検索結果

"Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.".

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"Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.".

日本語の概要は準備中です。原文の説明を表示しています。

"Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.".

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Configuring Salesforce opportunity management: sales stages, sales processes, opportunity record types, Path configuration, opportunity teams, opportunity splits, and forecasting categories. Use when setting up or restructuring the opportunity lifecycle for Sales Cloud. NOT for forecast types, rollup methods, quotas or manager adjustments — use admin/collaborative-forecasts. NOT for agreeing what the stages and exit criteria should be before any build — use admin/sales-process-mapping. NOT for CPQ pricing or product configuration. Keywords: BusinessProcess, sales process, OpportunityStage, ForecastCategoryName, PathAssistant, OpportunitySplit, OpportunitySplitType, OpportunityTeamMember, Opportunity.settings, enableOpportunityTeam, IsClosed, IsWon, IsPrivate.

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Analyze material forecasting and requirements planning systems including MRP/MRP II logic evaluation, BOM explosion accuracy, supplier lead time variability tracking, seasonal demand pattern detection, fabric yield and marker efficiency optimization, MOQ aggregation strategies.

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Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.

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サプライチェーン最適化コンサルティングスキル。需要予測、在庫最適化、調達戦略、 物流ネットワーク設計、S&OP(Sales and Operations Planning)を支援。 Use when optimizing supply chain operations, improving inventory management, designing logistics networks, or conducting supply chain risk assessments. Triggers: "supply chain", "在庫最適化", "inventory optimization", "demand forecasting", "S&OP", "procurement strategy", "logistics network".

Expert en budgétisation et prévisions (FP&A, variance analysis, rolling forecasts, DZ)

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Complete B2B sales system — from ICP to closed-won. Discovery frameworks, objection handling, pipeline management, forecasting, and deal acceleration. Works for any B2B company.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly pipeline health analysis with velocity metrics, stage distribution, forecasting, and actionable recommendations

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Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.

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Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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Token unlock schedule analysis and project treasury tracking — vesting cliffs, linear unlocks, team/investor/ecosystem token releases, treasury diversification, and sell pressure forecasting.

日本語の概要は準備中です。原文の説明を表示しています。

Global macro analysis framework (central bank policy transmission / FX forecasting / geopolitical risk / capital flows), used to build macro factor signals that drive cross-asset allocation.

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Plan resource capacity — workload analysis and utilization forecasting. Use when heading into quarterly planning, the team feels overallocated and you need the numbers, deciding whether to hire or deprioritize, or stress-testing whether upcoming projects fit the people you have.

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Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.

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Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist).

日本語の概要は準備中です。原文の説明を表示しています。

Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, building board forecasts, or when user mentions CRO, chief revenue officer, revenue strategy, sales model, ARR growth, NRR, expansion revenue, churn, pricing strategy, or sales capacity.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.

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Create, trade, and settle permissionless prediction markets on Solana. Use when building prediction market infrastructure, creating social media markets (Twitter/YouTube/DeFiLlama), setting up custom oracle resolution, P2P betting, or autonomous agent-driven forecasting. Supports V2 AMM, P2P (V3), and custom oracle markets with any SPL token collateral (including Token-2022).

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Projects your ROAS for the next 30, 60, and 90 days based on current performance trends, seasonality patterns from your historical data, and planned budget or campaign changes. Gives you a range with confidence intervals, not a single number pretending to be precise. Platform: Google and Meta.

日本語の概要は準備中です。原文の説明を表示しています。

Score observed price trend, confirmation, risk and participation without forecasting a price. Use when the user asks to momentum following with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

日本語の概要は準備中です。原文の説明を表示しています。