「time series」の検索結果

Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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。

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

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。

Multi-backend observability CLI for querying metrics, logs, and traces across 12 backends. This skill should be used when the user runs obz commands, constructs metric/log/trace queries, configures providers, parses obz JSON output, or troubleshoots obz errors. Covers all core commands (metric query/list/info/labels/label-values/series, log search, trace search/get), global flags, time formats, output modes, config files, and exit codes.

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。

时间序列分析师（时序）。专注于时间序列数据的趋势分析、季节性分解、预测建模和异常检测。

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。

"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'.".

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。

Audit manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival analysis.

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。

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.

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。

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.

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。

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

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。

Preview a signed-off design one release phase at a time — cut it down into a series of phase screens, each showing only what exists by that phase with the layout untouched, then put them on a scrubber you drag to watch the screen fill in release by release. Use when the user wants a Phase 1 / MVP version of an existing wireframe or mockup, phased or staged screens, a design sliced by release phase or story map, or wants to see what a screen looks like before later features land.

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。

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

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。

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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。

Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.

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Read MIKE+ .res1d results via the mike-plus MCP server — list contents, summarize peaks, extract a single time series, or check which nodes flood (peak water level vs ground level). Use when an agent needs to inspect or pull numbers from MIKE+ network results (node water levels, link/reach discharge, velocities, flooding). No MIKE+ license or installation required.

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。

Produce publication-style figures from MIKE+ .res1d results via the mike-plus MCP server — a stacked rainfall-runoff hydrograph, a single time series, a network map, a baseline-vs-scenario overlay, or a longitudinal profile (bed / crown / ground / max water level along a pipe string), in the house style (Arial 12, ticks inward, SI units, no title). Use when an agent must render MIKE+ results as a PNG without opening the GUI. No license required.

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。

Turn a timestamped rainfall CSV (design storm, gauge record, climate-scenario series) into a MIKE+ rainfall .dfs0 via the mike-plus MCP server, verified by read-back, ready to drive a model's rainfall boundary or to sit on top of a hydrograph figure. Use when the user brings rain as CSV or asks to run a model with a different storm. No license required (writing the file); wiring it into the model needs one.

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。

Analyze Profound AI-visibility and citation-tracking data when an authorized connector is available. Use for time-series brand mentions, cited prompts, competitor co-mentions, and AI-answer visibility trends.

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。

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

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。

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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。

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

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。

tespit etme
abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly Tespit.

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。

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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。

Sparse Identification of Nonlinear Dynamics (SINDy) — discover governing equations from time-series data. Builds sparse dynamical system models dx/dt = f(x) from measurements using PySINDy. Use when you have trajectory data and want to find the underlying ODE.

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。

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

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。