[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CXRDMZN8DRQ73ESGJZDC18":3,"skill-same:01M6CXRDMZN8DRQ73ESGJZDC18":55},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":35,"commitSha":36,"skillDirectory":5,"forksCount":37,"repositoryHtmlUrl":38,"repositoryHomepageUrl":6,"ownerHtmlUrl":39,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":40,"install":41,"resources":54},"01M6CXRDMZN8DRQ73ESGJZDC18","time-series-analysis",null,"Time series analysis and forecasting","ffsshhttiikk\u002Fopencode-agents-skills","ffsshhttiikk","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F226346827?v=4","01M6CWD9RR3Y7VRQX3MXYC8FYH","01M6CWD9WVPYAZW0J82G1VKGHY",3,"MIT","https:\u002F\u002Fgithub.com\u002Fffsshhttiikk\u002Fopencode-agents-skills\u002Fblob\u002Fmain\u002Ftime-series-analysis\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26],"time series","analysis","time","series","time series analysis","data","forecasting","opencode","intelligence","2026-11-10 10:12:30",false,"---\nname: time-series-analysis\ndescription: Time series analysis and forecasting\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: data-analysts\n  category: artificial-intelligence\n---\n\n## What I do\n\n- Analyze temporal data patterns\n- Build forecasting models\n- Detect trends and seasonality\n- Handle temporal dependencies\n- Create time series features\n- Evaluate forecast accuracy\n\n## When to use me\n\nUse me when:\n- Predicting future values\n- Analyzing trends over time\n- Detecting seasonality\n- Anomaly detection in time series\n- Resource planning and forecasting\n\n## Key Concepts\n\n### Time Series Components\n```python\nimport pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.seasonal import seasonal_decompose\n\n# Decomposition\ndecomposition = seasonal_decompose(ts, model='additive', period=12)\ndecomposition.plot()\nplt.show()\n\n# Components:\n# - Trend: Long-term direction\n# - Seasonality: Repeating patterns\n# - Cyclical: Longer-term oscillations\n# - Residual: Random noise\n```\n\n### Forecasting Models\n```python\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nfrom prophet import Prophet\n\n# ARIMA\nmodel = ARIMA(train, order=(1, 1, 1))\nfitted = model.fit()\nforecast = fitted.forecast(steps=30)\n\n# SARIMA (with seasonality)\nmodel = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12))\nfitted = model.fit()\n\n# Prophet (Facebook)\nmodel = Prophet(yearly_seasonality=True, weekly_seasonality=True)\nmodel.fit(df[['ds', 'y']])\nfuture = model.make_future_dataframe(periods=30)\nforecast = model.predict(future)\n```\n\n### Feature Engineering\n```python\ndef create_time_features(df):\n    df['year'] = df['date'].dt.year\n    df['month'] = df['date'].dt.month\n    df['day'] = df['date'].dt.day\n    df['dayofweek'] = df['date'].dt.dayofweek\n    df['quarter'] = df['date'].dt.quarter\n    df['is_weekend'] = df['date'].dt.dayofweek >= 5\n    \n    # Lag features\n    for lag in [1, 7, 14, 30]:\n        df[f'lag_{lag}'] = df['value'].shift(lag)\n    \n    # Rolling features\n    df['rolling_mean_7'] = df['value'].rolling(7).mean()\n    df['rolling_std_7'] = df['value'].rolling(7).std()\n    \n    return df\n```\n\n### Evaluation Metrics\n- MAE (Mean Absolute Error)\n- RMSE (Root Mean Square Error)\n- MAPE (Mean Absolute Percentage Error)\n- MASE (Mean Absolute Scaled Error)\n","## What I do\n\n- Analyze temporal data patterns\n- Build forecasting models\n- Detect trends and seasonality\n- Handle temporal dependencies\n- Create time series features\n- Evaluate forecast accuracy\n\n## When to use me\n\nUse me when:\n- Predicting future values\n- Analyzing trends over time\n- Detecting seasonality\n- Anomaly detection in time series\n- Resource planning and forecasting\n\n## Key Concepts\n\n### Time Series Components\n```python\nimport pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.seasonal import seasonal_decompose\n\n# Decomposition\ndecomposition = seasonal_decompose(ts, model='additive', period=12)\ndecomposition.plot()\nplt.show()\n\n# Components:\n# - Trend: Long-term direction\n# - Seasonality: Repeating patterns\n# - Cyclical: Longer-term oscillations\n# - Residual: Random noise\n```\n\n### Forecasting Models\n```python\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nfrom prophet import Prophet\n\n# ARIMA\nmodel = ARIMA(train, order=(1, 1, 1))\nfitted = model.fit()\nforecast = fitted.forecast(steps=30)\n\n# SARIMA (with seasonality)\nmodel = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12))\nfitted = model.fit()\n\n# Prophet (Facebook)\nmodel = Prophet(yearly_seasonality=True, weekly_seasonality=True)\nmodel.fit(df[['ds', 'y']])\nfuture = model.make_future_dataframe(periods=30)\nforecast = model.predict(future)\n```\n\n### Feature Engineering\n```python\ndef create_time_features(df):\n    df['year'] = df['date'].dt.year\n    df['month'] = df['date'].dt.month\n    df['day'] = df['date'].dt.day\n    df['dayofweek'] = df['date'].dt.dayofweek\n    df['quarter'] = df['date'].dt.quarter\n    df['is_weekend'] = df['date'].dt.dayofweek >= 5\n    \n    # 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