Agent skill · data analytics · aj-geddes
Time Series Analysis
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Why this skill is useful
Provides specific techniques and Python implementations for time series analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, statsmodels installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Time Series Analysis Overview Time series analysis examines data points collected over time to identify patterns, trends, and seasonality for forecasting and understanding temporal dynamics. When to Use Forecasting future values based on historical trends Detecting seasonality and cyclical patterns in data Analyzing trends over time in sales, stock prices, or website traffic Understanding autocorrelation and temporal dependencies Making time-based predictions with confidence intervals Decomposing data into trend, seasonal, and residual components Core Components Trend: Long-term directional movement Seasonality: Repeating patterns at fixed intervals Cyclicity: Long-term oscillations (non-fixed periods) Stationarity: Constant mean, variance over time Autocorrelation: Correlation with past values Key Techniques Decomposition: Separating trend, seasonal, residual components Differencing: Making data stationary ARIMA: AutoRegressive Integrated Moving Average models Exponential Smoothing: Weighted average of past values SARIMA: Seasonal ARIMA models Implementation with Python Stationarity Stationary: Mean, variance, autocorrelation constant over time Non-stationary: Trend or seasonal patterns present Solution: Differencing, log transformation, or detrending Model Selection ARIMA: Good for univariate forecasting SARIMA: Includes seasonal components Exponential Smoothing: Simpler, good for trends Prophet: Handles holidays and changepoints Evaluation Metrics MAE: Mean Absolute Error RMSE: Root Mean Squared Error MAPE: Mean Absolute Percentage Error Deliverables Decomposition analysis charts Stationarity test results ACF/PACF plots Fitted models with diagnostics Forecast with confidence intervals Accuracy metrics comparison
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/time-series-analysis