source: kdnuggets: 5 useful python scripts for time series analysis
level: technical
working with time series data often means dealing with irregular timestamps, unexpected spikes, and mixed signals from trend and seasonality. these five python scripts address those recurring problems. they read standard csv or excel files, apply configurable processing, and output clean results. the scripts are independent but can be used in sequence: resample first, then detect anomalies, decompose, forecast, and finally compare multiple series.
the resampling script aligns uneven data to a set frequency using pandas, filling gaps with forward-fill or interpolation. the anomaly detector flags outliers via z-score, iqr, or rolling statistics, with an optional chart. decomposition splits a series into trend, seasonal, and residual parts using statsmodels, supporting additive or multiplicative models. the forecasting script fits a sarima model, optionally auto-tuning parameters via aic, and reports error metrics on a held-out test set before projecting forward with confidence intervals.
the comparison script handles multiple series by computing pairwise correlations, cross-correlation lags, and summary statistics, then highlights the top correlated pairs in charts. each script includes a configuration section to set column names, frequencies, and method choices. they are meant to be tested on small samples before full runs and can be integrated into pipelines. the code is available on github with dependency lists in readme files.
why it matters: these scripts automate repetitive time series preprocessing and analysis steps, saving data scientists time and reducing manual errors in tasks like anomaly flagging and forecast generation.
source: kdnuggets: 5 useful python scripts for time series analysis