Hot Trend Lab: Real Data Trend and Cycle Decomposition
Hot topics usually have three time structures: a durable trend, a repeating cadence, and a residual shock that breaks the normal pattern. DeTime is useful here because it makes those structures explicit before the column turns them into a story.
Consider a model-release week. arXiv may show a category or query spike before the launch, Hugging Face may show a same-day snapshot jump, GitHub stars may accelerate after demos ship, and Wikimedia or market proxies may react later. Those signals are interesting together, but they answer different questions. The column keeps source coverage, freshness, query context, timestamp, and interpretation boundary next to each chart so a reader can tell what changed and what still needs outside evidence.
trend, season, residual, meta
What the reader gets
Each case study starts with a source protocol, then produces a table or figure that supports a specific editorial judgment:
| Component | What to read | Example question |
|---|---|---|
trend |
durable direction over the selected window | Is a topic still building after the launch week? |
season |
repeated cadence | Is the pattern mostly deadline or weekday rhythm? |
residual |
event-like deviation from the smooth baseline | Which week deserves a closer read? |
meta |
source, query, timestamp, coverage, limits | Can this chart support the sentence being written? |
The result is not a popularity scoreboard. A single source rarely measures quality, adoption, or economic value on its own.
Column map
Source Protocol
Source cards, reliability matrix, API limits, cache rules, and timestamp discipline.
arXiv Research Pulse
Category and query counts with deadline-aware interpretation.
Open Models and Developer Attention
Launch-to-retention reading across Hugging Face snapshots and GitHub star velocity.
Public Attention, Markets, and Crypto
A hub for public attention and market-proxy series, with market-structure boundaries.
Publishing Protocol
Monday scan, midweek verification, Friday publish, and residual-trigger rules.
Case-study notebooks
The pages below render notebook code, tables, figures, and captured outputs directly
from examples/notebooks/hot_trends/. Read each notebook for three things: why the
source is interesting, how to read the tables or axes, and what decision the
output can support.
| Rendered page | Primary judgment | Main boundary |
|---|---|---|
| 01 arXiv category pulse | which fields are structurally growing or spiking | paper count is not paper quality |
| 02 arXiv agent research pulse | which agent topics deserve a weekly read | query wording changes the sample |
| 03 Hugging Face open-model pulse | current public model attention and later retention | one snapshot cannot show momentum |
| 04 GitHub AI-agent star velocity | developer attention during the covered window | stars are not production adoption |
| 05 Wikipedia attention hype decay | public-attention decay after a burst | pageviews are attention, not importance |
| 06 crypto stablecoin liquidity pulse | price residuals next to stablecoin context | not a trading signal |
| 07 AI infrastructure market pulse | market-price proxy movement around AI infrastructure | valuation needs financial statements |
The rendered notebook index and the 00 overview notebook are appendix material for readers who want the full transcript list.
Install optional dependencies
python -m pip install -e .[dev,docs,notebook]
python -m pip install -r examples/hot_trends/requirements.txt
Then open:
jupyter lab examples/notebooks/hot_trends
Research scope
Use this column to analyze public attention, research activity, open-source activity, liquidity, and market-proxy time series. Adoption, quality, valuation, and investment decisions need separate evidence.