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Tool to explore regularly sampled time series

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Why This Matters

The Time Series Explorer (tseda) offers a powerful tool for analyzing regularly sampled time series data, enabling users to identify patterns, seasonality, and structural changes efficiently. Its integration of spectral analysis and change point detection benefits data scientists and industry professionals seeking deeper insights from temporal datasets, ultimately enhancing forecasting and decision-making capabilities.

Key Takeaways

Time Series Explorer ( tseda )

An application for time series exploration.

Overview

tseda lets you explore regularly sampled time series with a sampling frequency of one hour or greater. It is currently limited to 2,000 samples (this is configurable).

Three-Step Exploration Workflow

(a) Initial Assessment

Explore the distribution and spread of values using a kernel density estimate and box plot. You get to see the raw distribution of the values. The PACF and ACF provide clues about seasonality and autoregressive components.

(b) Decomposition Using Singular Spectral Analysis

On the basis of the sampling frequency, a window for SSA is determined. This is a heuristic assignment. For example:

Sampling Frequency Window Size Hourly 24 Monthly 12 Quarterly 4

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