Does it provide guidance on model validation?

Yes, it includes patterns for Time Series Cross-Validation and metrics like MAE, RMSE, and MAPE to ensure forecast reliability.

How does it assist with anomaly detection?

It offers a range of techniques from statistical Z-score and IQR methods to machine learning approaches like Isolation Forest for high-dimensional data.

Can this skill handle complex seasonal data?

Yes, it includes specialized workflows for SARIMA models and STL decomposition to isolate and analyze multiple seasonal patterns.

What Python libraries does this skill support?

The skill provides implementation patterns for major data science libraries including Statsmodels, Prophet, Scikit-learn, and TensorFlow/Keras.

Time Series Analyst

Name: Time Series Analyst
Author: organvm-iv-taxis

byorganvm-iv-taxis

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Data Science & ML

Analyzes temporal data to identify patterns, trends, and anomalies while building accurate forecasting models using statistical and machine learning methods.

The Time Series Analyst skill transforms Claude into a specialized data science assistant capable of handling complex temporal datasets. It provides rigorous workflows for assessing data stationarity, performing seasonal decomposition, and implementing a wide array of forecasting models ranging from classical statistical methods like SARIMA to modern machine learning approaches like Facebook Prophet and LSTMs. This skill is essential for developers and data scientists who need to extract actionable insights from time-ordered data, detect outliers, and predict future trends with high confidence.

Key Features

01Time series decomposition for trend and seasonality analysis

02Deep learning sequence modeling using LSTM architectures

03Statistical stationarity testing using ADF and KPSS

04Multi-method anomaly detection including Isolation Forest

052 GitHub stars

06Advanced forecasting with ARIMA, SARIMA, and Prophet

Use Cases

01Predicting financial market trends and asset price movements

02Planning resource capacity based on seasonal demand forecasting

03Detecting anomalies in system logs and IoT sensor data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add organvm-iv-taxis/a-i--skills time-series-analyst

For use in Claude.ai and ChatGPT

Key Features

01Time series decomposition for trend and seasonality analysis

02Deep learning sequence modeling using LSTM architectures

03Statistical stationarity testing using ADF and KPSS

04Multi-method anomaly detection including Isolation Forest

052 GitHub stars

06Advanced forecasting with ARIMA, SARIMA, and Prophet

Use Cases

01Predicting financial market trends and asset price movements

02Planning resource capacity based on seasonal demand forecasting

03Detecting anomalies in system logs and IoT sensor data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add organvm-iv-taxis/a-i--skills time-series-analyst

For use in Claude.ai and ChatGPT