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| <h1 id="multivariate-workflows">Multivariate workflows</h1> |
| <h2 id="supported-multivariate-methods">Supported multivariate methods</h2> |
| <p>Current multivariate-capable methods are:</p> |
| <ul> |
| <li><code>MSSA</code></li> |
| <li><code>MVMD</code></li> |
| <li><code>MEMD</code></li> |
| </ul> |
| <p>Channelwise methods that also accept <code>2D (T, C)</code> input:</p> |
| <ul> |
| <li><code>STD</code></li> |
| <li><code>STDR</code></li> |
| </ul> |
| <h2 id="python-example-mssa">Python example: MSSA</h2> |
| <pre><code class="language-python">import numpy as np |
| from detime import DecompositionConfig, decompose |
|
|
| t = np.arange(96, dtype=float) |
| series = np.column_stack( |
| [ |
| 0.03 * t + np.sin(2.0 * np.pi * t / 12.0), |
| -0.01 * t + 0.6 * np.sin(2.0 * np.pi * t / 12.0 + 0.4), |
| ] |
| ) |
|
|
| result = decompose( |
| series, |
| DecompositionConfig( |
| method="MSSA", |
| params={"window": 24, "rank": 8, "primary_period": 12}, |
| channel_names=["x0", "x1"], |
| ), |
| ) |
| </code></pre> |
| <p>Observed output from <code>examples/multivariate_mssa.py</code> on the current docs build:</p> |
| <pre><code class="language-text">trend shape: (96, 2) |
| modes shape: (8, 96, 2) |
| backend: python |
| </code></pre> |
| <p>Published raw stdout:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/multivariate/python_example_stdout.txt">python_example_stdout.txt</a></li> |
| </ul> |
| <h2 id="published-multivariate-comparison">Published multivariate comparison</h2> |
| <p>The current docs build also publishes a joint <code>MSSA</code> versus channelwise <code>STD</code> |
| walkthrough on a three-channel synthetic panel.</p> |
| <table> |
| <thead> |
| <tr> |
| <th>Channel</th> |
| <th>MSSA backend</th> |
| <th>STD backend</th> |
| <th style="text-align: right;">MSSA residual RMS</th> |
| <th style="text-align: right;">STD residual RMS</th> |
| <th style="text-align: right;">Mean abs trend gap</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td><code>sensor_a</code></td> |
| <td><code>python</code></td> |
| <td><code>native</code></td> |
| <td style="text-align: right;">0.0047</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0590</td> |
| </tr> |
| <tr> |
| <td><code>sensor_b</code></td> |
| <td><code>python</code></td> |
| <td><code>native</code></td> |
| <td style="text-align: right;">0.0038</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0260</td> |
| </tr> |
| <tr> |
| <td><code>sensor_c</code></td> |
| <td><code>python</code></td> |
| <td><code>native</code></td> |
| <td style="text-align: right;">0.0999</td> |
| <td style="text-align: right;">0.0000</td> |
| <td style="text-align: right;">0.0128</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Published experiment record:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/visual-multivariate/multivariate_summary.csv">multivariate_summary.csv</a></li> |
| </ul> |
| <p>Published example outputs:</p> |
| <p><img alt="MSSA multivariate components" src="../../assets/generated/tutorials/visual-multivariate/mssa_multivariate.png" /></p> |
| <p><img alt="Channelwise STD multivariate components" src="../../assets/generated/tutorials/visual-multivariate/std_channelwise_multivariate.png" /></p> |
| <p><img alt="Channel 0 trend overlay" src="../../assets/generated/tutorials/visual-multivariate/channel0_trend_overlay.png" /></p> |
| <h2 id="cli">CLI</h2> |
| <pre><code class="language-bash">python -m detime run \ |
| --method MSSA \ |
| --series examples/data/example_multivariate.csv \ |
| --cols x0,x1 \ |
| --param window=24 \ |
| --param rank=8 \ |
| --param primary_period=12 \ |
| --out_dir out/mssa_run \ |
| --output-mode summary |
| </code></pre> |
| <p>Published CLI stdout from the current docs build:</p> |
| <pre><code class="language-text">Running MSSA on examples/data/example_multivariate.csv... |
| Done. Results saved to out/mssa_run |
| </code></pre> |
| <p>Published output files:</p> |
| <ul> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/multivariate/example_multivariate_summary.json">example_multivariate_summary.json</a></li> |
| <li><a href="../../assets/generated/tutorials/cli-and-profiling/multivariate/command_stdout.txt">command_stdout.txt</a></li> |
| </ul> |
| <p>Representative summary excerpt from the current build:</p> |
| <pre><code class="language-json">{ |
| "mode": "summary", |
| "trend": { "shape": [120, 2] }, |
| "season": { "shape": [120, 2] }, |
| "components": { |
| "modes": { "shape": [8, 120, 2] } |
| }, |
| "meta": { |
| "backend_used": "python", |
| "channel_names": ["x0", "x1"] |
| } |
| } |
| </code></pre> |
| <p>When <code>output-mode</code> is <code>full</code>, multivariate results are written as wide CSV for |
| <code>2D</code> outputs plus <code>.npz</code> archives for <code>3D</code> components. In the published docs |
| build, <code>summary</code> mode is used to keep the artifact small and easy to inspect.</p> |
| <h2 id="where-to-go-next">Where to go next</h2> |
| <ul> |
| <li>Use <a href="../visual-multivariate/">Visual Multivariate Walkthrough</a> for the full |
| side-by-side interpretation of the figures above.</li> |
| <li>Use <a href="../cli-and-profiling/">CLI and Profiling</a> when you want the exact batch, |
| profile, schema, and recommendation commands that operate on repo-shipped |
| files.</li> |
| </ul></div> |
| </div> |
| </div> |
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