Can I use this for deep learning-based forecasting?

Yes, it includes implementation patterns for LSTM (Long Short-Term Memory) networks and provides guidance on sequence preparation for neural networks.

Does it include tools for detecting outliers?

Yes, the skill covers multiple anomaly detection techniques including Z-score analysis, IQR methods, and machine learning approaches like Isolation Forest.

How does it handle non-stationary data?

It provides automated testing procedures using ADF and KPSS, along with transformation techniques like differencing, log scales, and Box-Cox transformations.

What statistical models does this skill support?

The skill supports a wide range of models including ARIMA, SARIMA, Exponential Smoothing (Holt-Winters), and STL decomposition through libraries like statsmodels.

Time Series Analyst

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

byorganvm-iv-taxis

•

Data Science & ML

Analyzes and forecasts temporal data using statistical models and machine learning techniques to identify trends and anomalies.

The Time Series Analyst skill equips Claude with specialized knowledge for handling sequential, time-dependent data. It provides structured guidance on performing stationarity tests, seasonal decomposition, and implementing a wide array of forecasting models from classical ARIMA and SARIMA to modern machine learning approaches like Facebook Prophet and LSTM neural networks. This skill is ideal for developers and data scientists needing to implement rigorous temporal analysis, automate anomaly detection, or build predictive models for business metrics, system performance, or financial data.

Key Features

01Automated stationarity testing (ADF/KPSS) and data transformation methods

02Advanced ML modeling using Facebook Prophet and LSTM neural networks

03Statistical forecasting with ARIMA, SARIMA, and Exponential Smoothing

04Multi-method anomaly detection using Isolation Forest and rolling statistics

05Robust STL decomposition to isolate trends, seasonality, and residuals

062 GitHub stars

Use Cases

01Analyzing financial market trends and seasonal economic patterns

02Detecting infrastructure anomalies and performance regressions in system logs

03Predicting business KPIs and demand forecasting for inventory management

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

01Automated stationarity testing (ADF/KPSS) and data transformation methods

02Advanced ML modeling using Facebook Prophet and LSTM neural networks

03Statistical forecasting with ARIMA, SARIMA, and Exponential Smoothing

04Multi-method anomaly detection using Isolation Forest and rolling statistics

05Robust STL decomposition to isolate trends, seasonality, and residuals

062 GitHub stars

Use Cases

01Analyzing financial market trends and seasonal economic patterns

02Detecting infrastructure anomalies and performance regressions in system logs

03Predicting business KPIs and demand forecasting for inventory management

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