5 Useful Python Scripts for Time Series Analysis

2026-05-12 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

KDnuggets Contributing Editor Bala Priya C released five Python scripts on May 12, 2026, designed to streamline common time series analysis tasks. These scripts address challenges such as irregular data intervals, anomaly detection, trend and seasonality decomposition, forecasting, and multi-series comparison. Each script works with standard CSV or Excel inputs, producing clean outputs and offering straightforward configuration. The collection includes tools for resampling and aggregating data using pandas, detecting anomalies via z-score, IQR, or rolling statistics, decomposing series into trend, seasonal, and residual components with `statsmodels.tsa.seasonal.seasonal_decompose()`, forecasting with SARIMA models using `statsmodels.tsa.statespace.sarimax.SARIMAX` and optional AIC-based auto-ordering, and comparing multiple series through correlation and lag analysis.

Key takeaway

For data scientists and machine learning engineers working with time series, these Python scripts offer ready-to-use solutions for recurring analytical challenges. You should download the relevant script, install dependencies, configure it for your specific data, and test it on a sample before integrating it into your data pipelines. This approach can significantly reduce manual effort in data preparation, anomaly detection, decomposition, forecasting, and multi-series comparison.

Key insights

Five Python scripts simplify common time series analysis tasks, from data preparation to forecasting and comparison.

Principles

• Standardize irregular time series data for consistent analysis.
• Isolate components (trend, seasonality, residuals) for clearer insights.
• Automate model parameter selection to improve forecast reliability.

Method

The scripts utilize pandas for data handling and `statsmodels` for statistical modeling, employing methods like `resample()`, z-score/IQR for anomaly detection, `seasonal_decompose()`, and SARIMAX for forecasting.

In practice

• Use `ts_resampler.py` to align sensor readings to a uniform frequency.
• Apply `ts_anomaly_detector.py` to flag unusual spikes in transaction logs.
• Employ `ts_forecast.py` to predict future sales with confidence intervals.

Topics

• Time Series Analysis
• Python Scripts
• Data Resampling
• Anomaly Detection
• Time Series Decomposition

Code references

• balapriyac/data-science-tutorials

Best for: Data Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.