'forecasting' 검색 결과

Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.

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• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

여러 매장의 판매 데이터와 상품 특성으로 수요 예측, 안전 재고, 발주량을 검토합니다. 판촉 종료 후 수요 감소와 계절 상품의 가격 인하 시점도 계획합니다.

• 상품별 수요 예측 방법을 선택합니다
• 납기 변경에 따라 안전 재고를 검토합니다
• 판촉 중과 종료 후의 수요를 추산합니다

Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

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• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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Use Chronos Forecasting for pretrained time-series forecasting, Chronos-2 covariates, data validation, fine-tuning, evaluation, and deployment planning.

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Master the AI tools that transform financial operations. From bookkeeping to forecasting, manage money smarter and make data-driven financial decisions. Use when "finance, accounting, budgeting, forecasting, FP&A, bookkeeping, expense management, finance, accounting, budgeting, forecasting, fpa, expenses" mentioned.

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Survey existing forecasting code or models in a codebase — find gaps, stale models, and missing validation. Use when asked "what forecasting models do we have", "audit our forecasts", or "find stale models".

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Validate and benchmark a forecasting model — walk-forward CV, error metrics, baseline comparison. Use when asked "is this forecast any good", "validate a forecasting model", or "backtest the forecast".

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Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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Build AI time series forecasting systems. TRIGGERS - Use when user needs help with ai-forecasting-system related tasks.

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Set up, configure or troubleshoot Salesforce Collaborative Forecasts: forecast types, forecast categories, rollup methods, quota management, forecast hierarchy, manager adjustments, pipeline inspection integration. Trigger keywords: forecast type, forecast category, cumulative rollup, individual rollup, quota, forecast adjustment, forecast hierarchy, opportunity splits forecasting, pipeline forecast. NOT for configuring the opportunity stages, sales processes or split types themselves — use admin/opportunity-management. NOT for territory-based forecasting setup — use admin/enterprise-territory-management. NOT for Classic/Customizable Forecasting.

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Build AI time series forecasting systems. TRIGGERS - Use when user needs help with ai-forecasting-system related tasks.

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Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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Coordinate multiple AI agents by adjusting their communication structure and task assignments as workloads change. The skill describes performance monitoring, switching coordination patterns, forecasting agent capacity, and validating changes with rollback when performance worsens.

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• Choosing a coordination structure for multiple agents
• Redistributing tasks when agent workloads become uneven
• Forecasting how many agents a workload needs

Forecast sales and other time series with Google’s TimesFM without task-specific training. Prepare regularly spaced CSV data, export predicted values and uncertainty ranges, add explanatory variables, and evaluate forecasts against held-out observations.

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• Forecast monthly sales or revenue from CSV data
• Export predicted values with uncertainty ranges
• Forecast with additional explanatory variables

Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.

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• Fit regression models and interpret coefficients
• Compare models for binary outcomes or counts
• Forecast time series with prediction intervals

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

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• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

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• Classify time series and predict continuous outcomes
• Group time series with similar patterns
• Forecast future values

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

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• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes

Analyze SaaS sales opportunities, revenue forecast errors, and sales and marketing efficiency. Generates reports on stalled deals, pipeline risks, prediction bias, and customer acquisition economics for weekly sales reviews and quarterly business assessments.

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• Reviewing weekly sales coverage and stalled opportunities
• Finding dependence on a few large deals
• Comparing revenue forecasts with actual results over time

Build probability estimates from historical frequencies and supporting or opposing evidence. Structure forecasts with explicit assumptions and outcome criteria, update them as evidence changes, and assess past predictions using accuracy and confidence calibration.

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• Estimating probabilities for technology or economic developments
• Weighing evidence for and against a forecast
• Updating forecast probabilities when new evidence appears

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.

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Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

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