similarityraw_series

DeepSeek vs Threads: Pearson r 0.94, Spearman rho 1.00, Kendall tau 1.00. The levels line up much more than the day-to-day changes, so the relationship is easier to defend as a broad shape analogy than as a local-dynamics match. The weakest agreement appears in derivative similarity, so timing or regime differences probably matter.

Start with familiar coefficients, then inspect the time-series radar and component bars. This page does not treat the internal aggregate score as the main verdict.

Pearson r0.94linear level match

Spearman rho1.00rank-order match

Mutual info0.64nonlinear dependence

Diff r0.20local change match

Component mean 0.45 across 5 time-series-specific metrics.

Recommended next actions

Plot both series after z-score normalization to show the shared shape without scale differences.
Run rolling or windowed similarity if you expect the relationship to change over time.
Use structural-profile similarity when scales, frequencies, or observation modes differ too much for raw-shape comparison.
For cumulative or monotonic inputs, compare first differences or daily increments before making an analog claim.

Use this as an interpretable comparison summary. If you need alignment paths or distance matrices, pair the result with a lower-level similarity library.

Familiar statistics

Metric	Value
Pearson r	0.94
Spearman rho	1.00
Mutual info	0.64
Diff r	0.20
Kendall tau	1.00
Best-lag r	0.94

Notes and guardrails

Both inputs look strongly monotonic or cumulative, so EchoTime tightened the comparison around first differences, differenced DTW, and differenced spectra.
Levels or cumulative trajectories are much closer than the first differences, so local change-by-change agreement is weaker than the headline coefficients suggest.
Dynamic-time-warping similarity is stronger than direct shape correlation, suggesting similar patterns with shifted timing.

Compact agent context

{
  "type": "similarity",
  "headline": "DeepSeek vs Threads: Pearson r 0.94, Spearman rho 1.00, Kendall tau 1.00. The levels line up much more than the day-to-day changes, so the relationship is easier to defend as a broad shape analogy than as a local-dynamics match. The weakest agreement appears in derivative similarity, so timing or regime differences probably matter.",
  "overall_similarity": 0.43529273365545185,
  "qualitative_label": "low",
  "top_components": [
    {
      "name": "dtw_similarity",
      "score": 0.6080084177188803,
      "level": "high"
    },
    {
      "name": "trend_similarity",
      "score": 0.5402665601624719,
      "level": "moderate"
    },
    {
      "name": "spectral_similarity",
      "score": 0.46428886781680356,
      "level": "moderate"
    }
  ],
  "profile_similarity": null,
  "rolling_summary": null,
  "suggestions": [
    "Plot both series after z-score normalization to show the shared shape without scale differences.",
    "Run rolling or windowed similarity if you expect the relationship to change over time."
  ],
  "notes": [
    "Both inputs look strongly monotonic or cumulative, so EchoTime tightened the comparison around first differences, differenced DTW, and differenced spectra."
  ]
}