5 Python Scripts Tackle Time Series Analysis From Resampling to SARIMA Forecasting
Updated
Updated · KDnuggets · May 12
5 Python Scripts Tackle Time Series Analysis From Resampling to SARIMA Forecasting
3 articles · Updated · KDnuggets · May 12
Five reusable Python scripts are outlined for recurring time-series tasks: cleaning irregular timestamps, flagging anomalies, decomposing trend and seasonality, generating forecasts, and comparing multiple series.
Pandas and statsmodels drive the workflows, with CSV or Excel inputs, configurable settings, and outputs that include cleaned files, anomaly reports, decomposition charts, correlation tables, and forecast plots with 95% confidence intervals.
The anomaly script supports 3 methods—z-score, IQR, and rolling statistics—while the forecasting script fits SARIMA models, can auto-select parameters by AIC, and reports MAE and RMSE on a held-out test period.
The package is designed as a step-by-step pipeline: resample first, then detect anomalies, decompose the series, forecast future periods, and finally compare aligned series through correlations and cross-correlation lags.
Can simple Python scripts still outperform complex AI models for common business forecasting tasks?
Are code-based analysis tools facing extinction from new, powerful AI foundation models?
What is the hidden engineering cost of turning 'simple' data scripts into enterprise-grade solutions?