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|
| <h1 id="univariate-workflows">Univariate workflows</h1> |
| <h2 id="when-to-use-univariate-methods">When to use univariate methods</h2> |
| <p>Use the univariate path when one observed series is decomposed into trend, |
| seasonality, and remainder components.</p> |
| <p>Good starting methods:</p> |
| <ul> |
| <li><code>STL</code> when the seasonal period is known and the data are reasonably regular,</li> |
| <li><code>SSA</code> when you want a flexible subspace method,</li> |
| <li><code>STD</code> when you want blockwise seasonal-trend-dispersion structure,</li> |
| <li><code>WAVELET</code> when you want a multi-scale signal-processing view.</li> |
| </ul> |
| <h2 id="python-example-ssa">Python example: SSA</h2> |
| <pre><code class="language-python">import numpy as np |
| from detime import DecompositionConfig, decompose |
|
|
| t = np.arange(120, dtype=float) |
| series = 0.02 * t + np.sin(2.0 * np.pi * t / 12.0) |
|
|
| result = decompose( |
| series, |
| DecompositionConfig( |
| method="SSA", |
| params={"window": 24, "rank": 6, "primary_period": 12}, |
| ), |
| ) |
| </code></pre> |
| <p>Observed output from <code>examples/univariate_quickstart.py</code> on the current docs |
| build:</p> |
| <pre><code class="language-text">trend shape: (120,) |
| season shape: (120,) |
| residual shape: (120,) |
| backend: native |
| </code></pre> |
| <p>Published raw stdout:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/univariate/python_example_stdout.txt">python_example_stdout.txt</a></li> |
| </ul> |
| <h2 id="published-method-snapshot-on-the-repo-sample-series">Published method snapshot on the repo sample series</h2> |
| <p>The current docs build also records a direct <code>SSA</code> versus <code>STD</code> snapshot on |
| <code>examples/data/example_series.csv</code>.</p> |
| <table> |
| <thead> |
| <tr> |
| <th>Method</th> |
| <th>Backend</th> |
| <th style="text-align: right;">Trend std</th> |
| <th style="text-align: right;">Seasonal std</th> |
| <th style="text-align: right;">Residual RMS</th> |
| <th style="text-align: right;">Peak residual</th> |
| <th style="text-align: right;">Reconstruction error</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td><code>SSA</code></td> |
| <td><code>native</code></td> |
| <td style="text-align: right;">0.6917</td> |
| <td style="text-align: right;">0.7036</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0000</td> |
| </tr> |
| <tr> |
| <td><code>STD</code></td> |
| <td><code>native</code></td> |
| <td style="text-align: right;">0.6893</td> |
| <td style="text-align: right;">0.6558</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0000</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Published experiment record:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/univariate/method_snapshot.csv">method_snapshot.csv</a></li> |
| <li><a href="../../assets/generated/tutorials/univariate/method_snapshot.json">method_snapshot.json</a></li> |
| </ul> |
| <p>This sample series is intentionally smooth and periodic, so both methods |
| reconstruct it almost perfectly. Use the visual walkthroughs below when you |
| want a noisier signal that leaves a visible residual.</p> |
| <h2 id="saving-output-with-the-cli">Saving output with the CLI</h2> |
| <pre><code class="language-bash">python -m detime run \ |
| --method SSA \ |
| --series examples/data/example_series.csv \ |
| --col value \ |
| --param window=24 \ |
| --param rank=6 \ |
| --param primary_period=12 \ |
| --out_dir out/ssa_run \ |
| --output-mode summary \ |
| --plot |
| </code></pre> |
| <p>Published CLI stdout from the current docs build:</p> |
| <pre><code class="language-text">Running SSA on examples/data/example_series.csv... |
| Done. Results saved to out/ssa_run |
| </code></pre> |
| <p>Published output files:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/single-file/example_series_summary.json">example_series_summary.json</a></li> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/single-file/example_series_plot.png">example_series_plot.png</a></li> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/single-file/example_series_error.png">example_series_error.png</a></li> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/single-file/command_stdout.txt">command_stdout.txt</a></li> |
| </ul> |
| <p>Published example outputs:</p> |
| <p><img alt="Single-file SSA CLI plot" src="../../assets/generated/tutorials/cli-and-profiling/single-file/example_series_plot.png" /></p> |
| <p><img alt="Single-file SSA CLI residual" src="../../assets/generated/tutorials/cli-and-profiling/single-file/example_series_error.png" /></p> |
| <h2 id="where-to-go-next">Where to go next</h2> |
| <ul> |
| <li>Use <a href="../visual-univariate/">Visual Univariate Walkthrough</a> when you want one |
| noisier signal with clearer residual structure.</li> |
| <li>Use <a href="../visual-comparison/">Visual Method Comparison</a> when you want to compare |
| <code>SSA</code>, <code>STD</code>, <code>STDR</code>, and <code>STL</code> on the same series before choosing a |
| default.</li> |
| </ul></div> |
| </div> |
| </div> |
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