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CAiSE 2026 presentation — pmf-tsfm +Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt +KU Leuven · LIRIS

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[S-01] +OPENING ANCHOR LINE (rehearse cold): +"I'm going to show you something that took me a while to believe: +the best forecaster for your process model isn't one you trained — +and probably isn't one you should train."

+

Pause. Let it land. Then advance.

+`,raw:"",frontmatter:he,filepath:"",start:0,id:0,no:1},__clicksContext:null,__preloaded:!1}),ca={layout:"assertion-evidence",locator:"Process mining today",assertion:"Process discovery gives one static model, but processes drift"},de=T(ca),ua=A({get layout(){return de.layout},get transition(){return de.transition},get class(){return de.class},get clicks(){return de.clicks},get name(){return de.name},get preload(){return de.preload},slide:{content:"",revision:"-mcyqjn",frontmatterRaw:`layout: assertion-evidence +locator: Process mining today +assertion: Process discovery gives one static model, but processes drift +`,note:"",index:1,noteHTML:`

[S-02] +Q: Why isn't a discovered model enough?

+

Process discovery extracts a process model from event-log data — here a Directly-Follows +Graph (DFG): nodes are activities, arrows count directly-follows relations. +Same BPI2017 offer process, two periods: same representation, but the counts move +(Accepted → End falls 465 → 306). The process drifted. PMF starts here — learn those +temporal changes and forecast the next DFG.

+

Transition out: "If the model drifts, the natural question is HOW — and that splits into +two very different prediction problems." → S3 (PMF vs PPM)

+`,raw:"",frontmatter:de,filepath:"",start:48,id:1,no:2},__clicksContext:null,__preloaded:!1}),fa={layout:"two-col-evidence",locator:"Predictive process mining",assertion:"PPM forecasts one case · PMF the whole process model"},pe=T(fa),ha=A({get layout(){return pe.layout},get transition(){return pe.transition},get class(){return pe.class},get clicks(){return pe.clicks},get name(){return pe.name},get preload(){return pe.preload},slide:{content:"",revision:"-wz3gii",frontmatterRaw:`layout: two-col-evidence +locator: Predictive process mining +assertion: PPM forecasts one case · PMF the whole process model +`,note:"",index:2,noteHTML:`

[S-03] +Q: PMF vs the PPM I already know?

+

Same loan log, two different prediction problems. PPM is case-level: take ONE ongoing +application and predict its future — the next event, the remaining time, or the outcome +("will THIS loan be canceled?"). Horizon = the rest of that one case.

+

PMF is system-level: take a WINDOW of the whole log, and forecast the next process model — +how often each transition fires next, e.g. how often "offer sent → canceled" occurs across +ALL cases. Horizon = the near-term system future (conceptually weeks-to-months; the 7-day +experimental horizon is setup detail for S12 — don't say it here).

+

Same event log, different question. (Absorbed into the assertion — deliver in speech.)

+

60s. Transition out: "Both need data, but PMF needs something different — let me show you +the pipeline."

+`,raw:"",frontmatter:pe,filepath:"",start:135,id:2,no:3},__clicksContext:null,__preloaded:!1}),da={layout:"assertion-evidence",locator:"Pipeline",assertion:"Each directly-follows edge becomes a time series we forecast",clicks:2},ge=T(da),pa=A({get layout(){return ge.layout},get transition(){return ge.transition},get class(){return ge.class},get clicks(){return ge.clicks},get name(){return ge.name},get preload(){return ge.preload},slide:{content:"",revision:"-7ry8uy",frontmatterRaw:`layout: assertion-evidence +locator: Pipeline +assertion: Each directly-follows edge becomes a time series we forecast +clicks: 2 +`,note:"",index:3,noteHTML:`

[S-04] +Q: How does PMF become a forecasting problem?

+

Transition IN (from S3): "Both need data, but PMF needs something different — let me show you the pipeline."

+

DF is introduced here (full term "directly-follows (DF) relations"); DFG is already known from S2 — +use the abbreviation. Let the THREE CLICKS carry it; don't talk over them: +• click 0 — event log → one daily count series per DF edge (the navy "past" window; 3 arcs shown, +the point being we forecast ALL arcs, not just the hero). +• click 1 — forecast the next 7 days for every arc (amber tail; the "future" window). +• click 2 — sum each arc's 7 daily forecasts → the reassembled, FORECASTED DFG (≈316 vs held-out 315).

+

Keep it to "aggregate daily, forecast 7 days ahead." Do NOT mention stride / expanding window / +60-20-20 here — that's S12. "past/future" are a conceptual history→horizon motif, not the +experimental window.

+

Transition OUT (to S5): "So it's a forecasting problem. Why isn't it already solved?"

+`,raw:"",frontmatter:ge,filepath:"",start:243,id:3,no:4},__clicksContext:null,__preloaded:!1}),ga={layout:"assertion-evidence",locator:"The challenge",assertion:"DF series are hard: drift, intermittency, and heterogeneity"},me=T(ga),ma=A({get layout(){return me.layout},get transition(){return me.transition},get class(){return me.class},get clicks(){return me.clicks},get name(){return me.name},get preload(){return me.preload},slide:{content:"",revision:"kfn991",frontmatterRaw:`layout: assertion-evidence +locator: The challenge +assertion: "DF series are hard: drift, intermittency, and heterogeneity" +`,note:"",index:4,noteHTML:`

[S-05] +Q: What makes these series difficult?

+

Transition IN (from S4): "So it's a forecasting problem. Why isn't it already solved?"

+

Three data challenges, shown with REAL ground-truth series (no model lines yet): +① Drift — BPI2017 Sent → Canceled: a genuine level shift, fires ~46/day early, decays to ~3. +② Intermittent — BPI2019-1 Cancel → Record Invoice Receipt: ~80% zeros, sudden spikes to ~29. +③ Heterogeneous — the two panels are different logs with completely different shapes; DF +relations also differ within a single log. Neither is white noise, neither is a smooth trend.

+

Axes: x = day (stride = 1, so consecutive windows are consecutive days), y = value (the 7-day-ahead +forecast's last day). These two plots reappear on S14 with the model lines revealed — DO NOT show +any model line here.

+

45s. Transition OUT (to S6): "These are hard. So — do the methods we already have handle them?"

+`,raw:"",frontmatter:me,filepath:"",start:282,id:4,no:5},__clicksContext:null,__preloaded:!1}),ya={layout:"assertion-evidence",locator:"Prior work",assertion:"Machine Learning (ML) / Deep Learning (DL) underperform"},ye=T(ya),va=A({get layout(){return ye.layout},get transition(){return ye.transition},get class(){return ye.class},get clicks(){return ye.clicks},get name(){return ye.name},get preload(){return ye.preload},slide:{content:"",revision:"u47p7v",frontmatterRaw:`layout: assertion-evidence +locator: Prior work +assertion: Machine Learning (ML) / Deep Learning (DL) underperform +`,note:"",index:5,noteHTML:`

[S-06] +Q: Do the methods we already have handle these patterns?

+

Transition IN (from S5): "These are hard. So — do existing methods handle them?"

+

ML/DL don't win in general. Here is the prior benchmark's TOP ML — tuned XGBoost (Yu et al. 2025) — +on the SAME two series from S5: +• Drift: XGBoost stays high and misses the level-shift drop. +• Intermittent: XGBoost overfits badly — it hallucinates large values (40–71) where the truth is +mostly zero. A textbook overfit to a small, complex, heterogeneous signal. +Reason (callback to S5's challenges): small + complex + heterogeneous data makes from-scratch +ML/DL overfit. XGBoost is the benchmark's recommended top model and still loses — it becomes our ML +baseline going forward.

+

Honest nuance (speech only): the TSFM revealed on S14 wins by staying controlled (not overshooting), +not by capturing the rare spikes. Do NOT show any TSFM line here — that's S14.

+

45s. Transition OUT (to S7 complexity): "It's not bad luck — these series are statistically off the +charts, and the logs are tiny."

+`,raw:"",frontmatter:ye,filepath:"",start:338,id:5,no:6},__clicksContext:null,__preloaded:!1}),wa={layout:"assertion-evidence",locator:"Diagnosis",assertion:"DF series are shorter and statistically harder"},ve=T(wa),ba=A({get layout(){return ve.layout},get transition(){return ve.transition},get class(){return ve.class},get clicks(){return ve.clicks},get name(){return ve.name},get preload(){return ve.preload},slide:{content:"",revision:"-l930ys",frontmatterRaw:`layout: assertion-evidence +locator: Diagnosis +assertion: DF series are shorter and statistically harder +`,note:"",index:6,noteHTML:`

[S-07] +Q: Why do trained-from-scratch models struggle here?

+

Transition IN (from S6): "It's not bad luck — these series are statistically off the charts, +and the logs are tiny."

+

This is the quantitative WHY behind S6's overfitting. Two facts, one message: +• COMPLEX — across 7 paper complexity metrics, three stand out: Transition, Shifting, +Non-Gaussianity. The paper reports these as HIGHER than the 21 public forecasting +benchmarks (Li et al. 2025). Say it qualitatively — we don't plot the benchmark +numbers (not in our repo); the amber axes + badge carry it. +• SMALL — every log is one short multivariate series: only 307–726 daily steps, split +expanding-window, with up to 149 DF variables. Sepsis is the extreme (999 cases / +16,009 events over 459 days) — the sparsity that drives its later ER failure (backup).

+

Together: complex signal + little data → from-scratch ML/DL overfit (exactly S6). Numbers are +paper-faithful (stats_log.tex / stats_df.tex). Don't re-explain S6's overfit — prove it.

+

45-60s. Transition OUT (to S8 TSFM): "Small, complex, heterogeneous data is exactly where a +model you don't train might win."

+`,raw:"",frontmatter:ve,filepath:"",start:397,id:6,no:7},__clicksContext:null,__preloaded:!1}),ka={layout:"assertion-evidence",locator:"Forecasting today",assertion:"Foundation models are the new direction in forecasting"},we=T(ka),Sa=A({get layout(){return we.layout},get transition(){return we.transition},get class(){return we.class},get clicks(){return we.clicks},get name(){return we.name},get preload(){return we.preload},slide:{content:"",revision:"9w80or",frontmatterRaw:`layout: assertion-evidence +locator: Forecasting today +assertion: Foundation models are the new direction in forecasting +`,note:"",index:7,noteHTML:`

[S-08] +Q: What is the current direction in forecasting? (And are we forecasting with an LLM?)

+

Transition IN (from S7): "Small, complex, heterogeneous data is exactly where a model you +don't train might win."

+

THE load-bearing slide (outline: the single slide that, if cut, most weakens the talk — without +it half the room thinks we fine-tuned GPT). One message: the current direction in forecasting is +foundation models. Just as an LLM is a foundation model for language (text → text), a Time Series +Foundation Model (TSFM) is one for time series (time series → time series) — same recipe (pretrain +at scale, then generalize across tasks), different data. So we are NOT using an LLM.

+

Two terms defined here so later slides can lean on them: +• zero-shot — run the pretrained model on a new log, no training. +• fine-tuning — keep training it on task data. (LoRA / full variants defined later, S11/S15.)

+

45s. Transition OUT (to S9 — Why TSFMs for PMF): "Same recipe as LLMs. So why would that help +our problem?"

+`,raw:"",frontmatter:we,filepath:"",start:480,id:7,no:8},__clicksContext:null,__preloaded:!1}),_a={layout:"assertion-evidence",locator:"The bet",assertion:"Foundation models are built not to overfit"},be=T(_a),Ma=A({get layout(){return be.layout},get transition(){return be.transition},get class(){return be.class},get clicks(){return be.clicks},get name(){return be.name},get preload(){return be.preload},slide:{content:"",revision:"-cmtfwf",frontmatterRaw:`layout: assertion-evidence +locator: The bet +assertion: Foundation models are built not to overfit +`,note:"",index:8,noteHTML:`

[S-09] +Q: Why should a generic forecaster beat a specialized one?

+

Transition IN (from S8): "Same recipe as LLMs. So why would that help our problem?"

+

Callback to S7: our DF series are both COMPLEX and SMALL — only 307–726 daily points per log — +exactly where a model trained from scratch overfits.

+

LOAD-BEARING line (say aloud): "Specialized models overfit on small heterogeneous PMF data. +Foundation models, pretrained on millions of diverse series, are designed to not."

+

Caveat (critique constraint): "No event logs in pretraining, to our knowledge" — so this is +genuinely zero-shot transfer; we test whether generic forecasting knowledge carries to DF series. +(Say "no event logs in pretraining, to our knowledge" — NOT "no process data".)

+

~35s. Transition OUT (to S10 — Three questions): "We have a candidate. Here's what we ask of it."

+`,raw:"",frontmatter:be,filepath:"",start:574,id:8,no:9},__clicksContext:null,__preloaded:!1}),Ta={layout:"center"},ke=T(Ta),Ea=A({get layout(){return ke.layout},get transition(){return ke.transition},get class(){return ke.class},get clicks(){return ke.clicks},get name(){return ke.name},get preload(){return ke.preload},slide:{content:"",revision:"-ohvnea",frontmatterRaw:`layout: center +`,note:"",index:9,noteHTML:`

[S-10] +Q: What three things does this talk actually test?

+

Transition IN (from S9): "We have a candidate. Here's what we ask of it."

+

30s scaffold for the results. Names (Chronos / MOIRAI / TimesFM, LoRA, Entropic Relevance) +land on later slides, not here. Standard terminology only: zero-shot (NOT off-the-shelf), +fine-tuning (NOT adapting).

+

Q1 is forecasting accuracy vs the strongest PMF baselines ONLY (seasonal-naive + tuned +XGBoost) — the on-slide line is trimmed to "give better DF time-series forecasts?"; say the +"vs the strongest PMF baselines" comparator ALOUD. Do NOT claim process-model quality here — +that is Q3 / ER. +Q2 ("further") = does fine-tuning add on top of the zero-shot win. +Q3 (amber) = the open one: does a better forecast translate to a better forecasted process model?

+

Maps to the results (speaker-notes only): Q1 -> S13 (zero-shot beats both baselines), +Q2 -> S15 (fine-tuning marginal), Q3 -> S16 (ER parity).

+

Transition OUT (into S11 — The candidates): "Three questions. Here's what we point at them."

+`,raw:"",frontmatter:ke,filepath:"",start:659,id:9,no:10},__clicksContext:null,__preloaded:!1}),Oa={layout:"assertion-evidence",locator:"Model coverage",assertion:"3 model families, 12 variants, 3 settings"},Se=T(Oa),Aa=A({get layout(){return Se.layout},get transition(){return Se.transition},get class(){return Se.class},get clicks(){return Se.clicks},get name(){return Se.name},get preload(){return Se.preload},slide:{content:"",revision:"526d7k",frontmatterRaw:`layout: assertion-evidence +locator: Model coverage +assertion: 3 model families, 12 variants, 3 settings +`,note:"",index:10,noteHTML:`

[S-11] +Q: Which models, and what settings?

+

Key message: we tried a lot — 3 model families across 3 settings (12 zero-shot variants).

+

Names stay ON SCREEN, not in the voice (attention-risk #1 — let the timeline carry the load). +Say the shape, not the roster: each family has shipped several generations; the latest of each +(amber) is what the results use, and the trend is "newer = smaller, yet better" — MOIRAI-2.0 is just +~11.4M params. univariate throughout (Yu 2025). LoRA = Low-Rank Adaptation, small trainable adapters +on attention (full form is on the slide). 45s.

+

Transition IN (from S10 three questions): "Three questions. Here's what we point at them." +Transition OUT (to S12 experimental setup): "That's the coverage — now exactly how we evaluated it." +(The "...no event logs in pretraining ... point them at DF time series" line belongs at the results +entry, S13, not here.)

+`,raw:"",frontmatter:Se,filepath:"",start:730,id:10,no:11},__clicksContext:null,__preloaded:!1}),La={layout:"assertion-evidence",locator:"Experimental setup",assertion:"Each step adds one day, then re-forecasts the next seven"},_e=T(La),Pa=A({get layout(){return _e.layout},get transition(){return _e.transition},get class(){return _e.class},get clicks(){return _e.clicks},get name(){return _e.name},get preload(){return _e.preload},slide:{content:"",revision:"-9zhh4r",frontmatterRaw:`layout: assertion-evidence +locator: Experimental setup +assertion: Each step adds one day, then re-forecasts the next seven +`,note:"",index:11,noteHTML:`

[S-12] +Q: How exactly did you evaluate? (And was it fair?)

+

Transition IN (from S11 candidates): "Before the results, one slide on exactly how we tested."

+

This slide OWNS the window/stride detail kept off S4. Reconciliation: S4 says only "aggregate +daily, forecast 7 days ahead"; S12 adds the protocol — expanding window, stride = 1 day. Nothing +on S4 contradicts this.

+

Deliver the assumption -> design-choice mapping ALOUD (it is intentionally NOT on the slide):

+
    +
  • you re-plan as new data arrives each day -> stride = 1 day
    • +
  • you care about the coming week -> 7-day horizon
    • +
  • you never throw history away -> expanding (not sliding) window +Fairness (the Callout / key message): the SAME expanding window and 7-day horizon apply to every +model — baselines and TSFMs alike — and the two baselines are the STRONGEST from our prior +benchmark (seasonal-naive + XGBoost), not weak strawmen. Univariate inference throughout.
    • +
+

45s. Transition OUT (to S13 — zero-shot results): "With that setup, here are the zero-shot results."

+`,raw:"",frontmatter:_e,filepath:"",start:818,id:11,no:12},__clicksContext:null,__preloaded:!1}),Ca={layout:"assertion-evidence",locator:"Results",assertion:"The latest zero-shot TSFMs beat both baselines on every log"},Me=T(Ca),$a=A({get layout(){return Me.layout},get transition(){return Me.transition},get class(){return Me.class},get clicks(){return Me.clicks},get name(){return Me.name},get preload(){return Me.preload},slide:{content:"",revision:"-xehx2r",frontmatterRaw:`layout: assertion-evidence +locator: Results +assertion: The latest zero-shot TSFMs beat both baselines on every log +`,note:"",index:12,noteHTML:`

[S-13] +Q: Does Q1 hold — do zero-shot TSFMs beat the baselines?

+

Transition in: "Three families. Twelve variants. No event logs in pretraining. +Here's what happens when you point them at DF time series."

+

~2 min. Say "Mean Absolute Error" on first use (kicker carries MAE). CRITIQUE CONSTRAINT — +open with the baseline framing verbatim (the on-slide caption was dropped; deliver it aloud): +"We compare against the two strongest baselines from our prior benchmark; full ranking +on backup." Pre-empts cherry-picking.

+

Callouts (click 1 then 2): (1) the 3 latest TSFMs average −21% MAE vs the best baseline. +(2) across ALL twelve zero-shot variants × 4 logs, 92% of results beat the best baseline +(−15% mean); only four older/smaller models miss, all on BPI2017.

+

The three latest models (Chronos-2, MOIRAI-2.0, TimesFM-2.5) win MAE on all four logs — +including Sepsis (MOIRAI-2.0 ↓28%). Sepsis is NOT a MAE exception; it only becomes the +hard case later, on ER (beat 9). Don't undercut the MAE win here.

+

Q&A defense: some older/smaller variants don't beat the baselines on BPI2017 (paper +p.11) — that's why the bars show the latest of each family.

+

Transition out (into 7b): "Same data, same window — here's what that win looks like."

+`,raw:"",frontmatter:Me,filepath:"",start:889,id:12,no:13},__clicksContext:null,__preloaded:!1}),Ra={layout:"assertion-evidence",locator:"Results",assertion:"Zero-shot tracks the drift, stays controlled on sparsity"},Te=T(Ra),Ia=A({get layout(){return Te.layout},get transition(){return Te.transition},get class(){return Te.class},get clicks(){return Te.clicks},get name(){return Te.name},get preload(){return Te.preload},slide:{content:"",revision:"-vf40h6",frontmatterRaw:`layout: assertion-evidence +locator: Results +assertion: Zero-shot tracks the drift, stays controlled on sparsity +`,note:"",index:13,noteHTML:`

[S-14] +Q: What does the zero-shot win look like on real DF patterns?

+

Transition IN (from S13 bars): "Same data, same window — here's what that win looks like."

+

THE CALLBACK MOMENT — the audience remembers these two plots from S5/S6 (truth only, then XGBoost +failing). One click reveals the MOIRAI-2.0 line on BOTH, with the amber marks. Don't rush the click. +• Drift (left, amber box): "Same plot, same window. The TSFM tracks the drop XGBoost missed." +MOIRAI-2.0 misses the initial drop, then catches up at the troughs — online adaptation, NO +retraining. The box frames where it comes down while XGBoost stays stuck high. +• Sparsity (right, amber arrow) — the HONEST beat: MOIRAI-2.0 holds near zero. It avoids +XGBoost's false bursts (XGBoost hallucinated 40–71 where truth is mostly 0; MOIRAI-2.0 MAE 2.0, +−89%), but it does NOT catch the rare spikes. The arrow marks it: down to ~0, still present. +"Not a miracle spike detector — on a near-empty signal, the honest answer is to stay near zero."

+

Mechanism (if asked): the TSFM adapts via an EXPANDING historical-context window at inference — +no retraining (paper §4.1).

+

~1 min. Transition OUT (to S15 fine-tuning): "TSFMs win zero-shot. Natural next question: can we +make them better?"

+`,raw:"",frontmatter:Te,filepath:"",start:937,id:13,no:14},__clicksContext:null,__preloaded:!1}),Da={layout:"assertion-evidence",locator:"Fine-tuning",assertion:"Fine-tuning barely helps"},Ee=T(Da),Na=A({get layout(){return Ee.layout},get transition(){return Ee.transition},get class(){return Ee.class},get clicks(){return Ee.clicks},get name(){return Ee.name},get preload(){return Ee.preload},slide:{content:"",revision:"z8qzob",frontmatterRaw:`layout: assertion-evidence +locator: Fine-tuning +assertion: Fine-tuning barely helps +`,note:"",index:14,noteHTML:`

[S-15] Q: Does fine-tuning (Q2) help? Is it worth it?

+

~1 min 30s. The clean-negative beat before S16's low-energy ER concession. +Transition in: "TSFMs win zero-shot. Natural next question — can we make them better?"

+

Panel order (logs are small on screen — name them aloud): top-left BPI2017, top-right BPI2019-1, +bottom-left Sepsis, bottom-right Hospital Billing.

+

Read the slope: most lines hug the 1.0 zero-shot baseline (gray bundle — fine-tuning barely moves +accuracy), while a few full-FT lines shoot up in amber (overfitting on small logs): +MOIRAI-1.1-R-large +87% on BPI2019-1, +31% on Sepsis; Chronos-2 +16% on Sepsis. Click reveals the +tally: across all 36 LoRA + full fine-tuning runs, 19 (53%) landed worse than zero-shot. The few real +gains (Sepsis full-FT −10 to −13%) are small and dataset-dependent.

+

COST — speak it, do NOT put on slide (no wall-clock logged, no fabricated ratio): +"And it never comes for free. Zero-shot is one forward pass per window. LoRA and full fine-tuning each +add a whole training stage — repeated for every model and every log — and in the worst case full +fine-tuning nearly doubled the error. The accuracy payoff is a coin-flip and sometimes catastrophic. +So at PMF data scale, skip it."

+

Transition out: "Fine-tuning isn't the win. So does the forecasting win even translate into a better +process model?"

+`,raw:"",frontmatter:Ee,filepath:"",start:1007,id:14,no:15},__clicksContext:null,__preloaded:!1}),qa={layout:"assertion-evidence",locator:"Evaluation",assertion:"Forecasting beats reuse, but better forecasts don't make better models"},Oe=T(qa),xa=A({get layout(){return Oe.layout},get transition(){return Oe.transition},get class(){return Oe.class},get clicks(){return Oe.clicks},get name(){return Oe.name},get preload(){return Oe.preload},slide:{content:"",revision:"-fv989m",frontmatterRaw:`layout: assertion-evidence +locator: Evaluation +assertion: Forecasting beats reuse, but better forecasts don't make better models +`,note:"",index:15,noteHTML:`

[S-16] +Q: Does the forecasting win translate into a better process model?

+

Transition in: "But forecasting accuracy isn't the only thing we care about in PM." +OPEN WITH THE QUESTION, not the result — S14→S16 is the talk's lowest-energy moment.

+

Introduce the term: say "Entropic Relevance" in full on first use (the on-slide line carries it +full-form-first); use "ER" thereafter. One line: ER = expected bits to encode a log trace under the +forecasted model; lower = better; Truth is the floor.

+

Two findings (revealed in sequence with the two callouts):

+
    +
  1. v-click 1 — Forecast ≫ reuse: the Training bar (reuse the historical/discovered model) towers at +5.83 while EVERY forecast lands ~2.1–2.5 → forecasting has value (callback to discovery's static +model in S2).
    2. +
  2. v-click 2 — TSFMs ≈ baselines: the three FMs (2.43 / 2.52 / 2.39) are no better than the two +baseline forecasts (2.17 / 2.12); the differences are small → a better forecast did NOT yield a +better process model (answers Q3).
    3. +
+

~1 min 30s. THE LOAD-BEARING SLIDE. +Memorized rebuttal — deliver in SPEECH, not on slide (verbatim from SLIDES.md):

+

"ER parity means we're not producing better DFG structures — we're producing +DFGs with better edge weights. For tasks where the forecast itself is the +deliverable — capacity planning, drift detection, anomaly baselines — +edge-weight accuracy IS the contribution. For tasks where you need a different +process model, ER says the bottleneck is now the DFG representation, +not the forecaster."

+

Do not skip this line. Rehearse it cold.

+

Transition out (hands the "bottleneck has moved" framing to S17): "So the forecasting is solved — +which means the open problem has moved somewhere else."

+`,raw:"",frontmatter:Oe,filepath:"",start:1051,id:15,no:16},__clicksContext:null,__preloaded:!1}),Fa={layout:"assertion-evidence",locator:"Discussion",assertion:"The bottleneck has moved from forecasting accuracy to process-aware representation"},Ae=T(Fa),Ha=A({get layout(){return Ae.layout},get transition(){return Ae.transition},get class(){return Ae.class},get clicks(){return Ae.clicks},get name(){return Ae.name},get preload(){return Ae.preload},slide:{content:"",revision:"wbr8ne",frontmatterRaw:`layout: assertion-evidence +locator: Discussion +assertion: The bottleneck has moved from forecasting accuracy to process-aware representation +`,note:"",index:16,noteHTML:`

[S-17] +Q: Where does process model forecasting go from here?

+

Transition in (from S16): "So the forecasting is solved — the open problem has moved somewhere else."

+

LEAD WITH THE HEADER (say it first): the bottleneck has moved from forecasting accuracy +(solved — zero-shot TSFMs beat the baselines) to PROCESS-AWARE REPRESENTATION. DFGs capture +only the control-flow / workflow aspect — a lossy target (callback to S16's ER parity). This +slide OWNS the "bottleneck has moved" line — S18 must not repeat it.

+

Then the three future directions (paper §Discussion):

+
    +
  1. Beyond control flow — directly-follows is a control-flow-only view; enrich to other process +dimensions (resources, bottlenecks, decisions) and richer relations (loops, long-range deps).
    2. +
  2. Smarter adaptation — fine-tuning gains were marginal at this data scale; resource/method +constraints limited what we tried; better adaptation methods are open.
    3. +
  3. Larger log corpora — only four logs; larger, higher-quality collections would generalize the +findings and could support a process-native foundation model.
    4. +
+

Also mention aloud (kept off-slide): concept drift is a key challenge — combine drift detection +with incremental / lightweight fine-tuning for adaptive retraining (paper §Discussion).

+

~1 min. Transition out (to S18 Takeaways): "So what do we actually walk away with?"

+`,raw:"",frontmatter:Ae,filepath:"",start:1108,id:16,no:17},__clicksContext:null,__preloaded:!1}),ja={layout:"assertion-evidence",locator:"Takeaways",assertion:"Off-the-shelf forecasting is strong enough to build on"},Le=T(ja),za=A({get layout(){return Le.layout},get transition(){return Le.transition},get class(){return Le.class},get clicks(){return Le.clicks},get name(){return Le.name},get preload(){return Le.preload},slide:{content:"",revision:"sile38",frontmatterRaw:`layout: assertion-evidence +locator: Takeaways +assertion: Off-the-shelf forecasting is strong enough to build on +`,note:"",index:17,noteHTML:`

[S-18] +Q: What do I walk away with — and what do I do next?

+

Transition in: "So what do we walk away with?" ~75s. Two audiences: practitioners (what to do) +and the field (what we learned).

+

Practitioner — ONE point: these models are small. Run them on a laptop or a cheap GPU, get a +forecast fast, little compute. You CAN fine-tune, but at PMF data scale it's rarely worth it.

+

Two signals:

+
    +
  1. Zero-shot is the new PMF default. CRITIQUE CONSTRAINT — say the hedge verbatim: +"four logs is not a paradigm" — but a strong enough signal that the right new default is to +TRY a zero-shot TSFM first.
    2. +
  2. Fine-tuning is marginal at this data scale (callback to S15's flat slope).
    3. +
+

Do NOT say the "bottleneck has moved" line here (that's S17). No artifact/code links here (S19).

+

CLOSING ANCHOR — rehearse COLD, deliver in SPEECH (not on slide): +"Zero-shot TSFMs are the new PMF default. Four logs is not a paradigm — but it is a strong enough +signal that the right new default is to try a zero-shot TSFM first. The deeper signal is that +time-series forecasting just became cheap enough that process mining can borrow from it without +paying the training tax. What else can we borrow?"

+

Transition out (to S19 artifacts): "And all of this is yours to run."

+`,raw:"",frontmatter:Le,filepath:"",start:1162,id:17,no:18},__clicksContext:null,__preloaded:!1}),Ba={layout:"two-col-evidence",locator:"Artifacts",assertion:"Public, reproducible, and live to try right now"},Pe=T(Ba),Wa=A({get layout(){return Pe.layout},get transition(){return Pe.transition},get class(){return Pe.class},get clicks(){return Pe.clicks},get name(){return Pe.name},get preload(){return Pe.preload},slide:{content:"",revision:"yrsvht",frontmatterRaw:`layout: two-col-evidence +locator: Artifacts +assertion: Public, reproducible, and live to try right now +`,note:"",index:18,noteHTML:`

[S-19] +~1.5–2 min. THE DEMO BEAT — the work is reproducible and usable.

+

Transition in: "Everything I've shown is public and runs out of the box."

+

ONE click plays the ~29s screencast (once, no loop) — a real recording of the live app. +What it walks through: +• Forecast vs actual future — the bundled backtest, forecast DFG beside the held-out truth. +• Accuracy — ER / MAE / RMSE against that real future (ER truth = the floor to beat). +• Diff — where the forecast matched, missed (amber), or over-called (red): absolute, then relative %. +• Upload your own log — the live ZeroGPU path, forecast vs the last-known window. +Talk over it as it plays (~29s); let it finish before the next click advances the slide.

+

Point at the QR: "scan it now — the live Space, Chronos-2 on HF ZeroGPU, is in your +pocket for the Q&A; upload your own log."

+

Reproducibility: the same code path runs on a laptop (MPS), a CUDA box, or the HPC +cluster (H100 via Slurm) — nothing in the paper needs special hardware.

+`,raw:"",frontmatter:Pe,filepath:"",start:1233,id:18,no:19},__clicksContext:null,__preloaded:!1}),Va={layout:"end",class:"ty-light"},Ce=T(Va),Ua=A({get layout(){return Ce.layout},get transition(){return Ce.transition},get class(){return Ce.class},get clicks(){return Ce.clicks},get name(){return Ce.name},get preload(){return Ce.preload},slide:{content:"",revision:"9e006l",frontmatterRaw:`layout: end +class: ty-light +`,note:"",title:"Thank you",level:1,index:19,noteHTML:`

[S-20] +10 min Q&A. Backup slides follow, hidden from ToC (each backup carries hideInToc: true). +Hostile-Q answers rehearsed cold — see SLIDES.md "Required backup slides" + the Q&A defense section.

+`,raw:"",frontmatter:Ce,filepath:"",start:1307,id:19,no:20},__clicksContext:null,__preloaded:!1}),Ga={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"Both baselines are the strongest from our prior benchmark"},$e=T(Ga),Qa=A({get layout(){return $e.layout},get transition(){return $e.transition},get class(){return $e.class},get clicks(){return $e.clicks},get name(){return $e.name},get preload(){return $e.preload},slide:{content:"",revision:"-tcl4ng",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: Both baselines are the strongest from our prior benchmark +`,note:"",index:20,noteHTML:`

speaker note: Honest answer to "why only two baselines?" We did not cherry-pick — these are the top non-trained and top trained methods from the external Yu 2025 benchmark. The full ranking table lives in that benchmark, not in this deck; happy to point to it offline.

+`,raw:"",frontmatter:$e,filepath:"",start:1343,id:20,no:21},__clicksContext:null,__preloaded:!1}),Ja={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"Latest zero-shot TSFMs lead MAE across all variants"},Re=T(Ja),Ka=A({get layout(){return Re.layout},get transition(){return Re.transition},get class(){return Re.class},get clicks(){return Re.clicks},get name(){return Re.name},get preload(){return Re.preload},slide:{content:"",revision:"-5sgmi8",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: Latest zero-shot TSFMs lead MAE across all variants +`,note:"",index:21,noteHTML:`

speaker note: The full MAE table behind the S13 headline bars — paper Table 4, all 14 variants across the four logs. The headline compared the latest TSFM per family against the two baselines; this is the complete field. The story holds variant by variant: the latest Chronos / MOIRAI / TimesFM are best or statistically competitive on every log, and the occasional baseline-level number sits with the small/older variants. Amber marks the best (lowest) per log; gray rows are the two baselines.

+`,raw:"",frontmatter:Re,filepath:"",start:1372,id:21,no:22},__clicksContext:null,__preloaded:!1}),Ya={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"RMSE confirms the MAE ranking on every log"},Ie=T(Ya),Xa=A({get layout(){return Ie.layout},get transition(){return Ie.transition},get class(){return Ie.class},get clicks(){return Ie.clicks},get name(){return Ie.name},get preload(){return Ie.preload},slide:{content:"",revision:"-vduq00",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: RMSE confirms the MAE ranking on every log +`,note:"",index:22,noteHTML:`

speaker note: Same field, RMSE instead of MAE — sourced from the paper's dedicated RMSE table (Table 5), so it is camera-ready faithful. Switching the error metric does not change the story: the latest zero-shot TSFM per family is best or statistically competitive on every one of the four logs, the same ranking as MAE.

+`,raw:"",frontmatter:Ie,filepath:"",start:1388,id:22,no:23},__clicksContext:null,__preloaded:!1}),Za={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"LoRA and full fine-tuning rarely beat zero-shot"},De=T(Za),ei=A({get layout(){return De.layout},get transition(){return De.transition},get class(){return De.class},get clicks(){return De.clicks},get name(){return De.name},get preload(){return De.preload},slide:{content:"",revision:"-2h1omx",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: LoRA and full fine-tuning rarely beat zero-shot +`,note:"",index:23,noteHTML:`

speaker note: [S-backup-FT] Paper Table 6 — the detail behind the S15 slope. Five fine-tunable models (Chronos-2 has no LoRA, hence two rows); MAE and RMSE per log for zero-shot vs LoRA vs full tune, grouped by model. Gains over zero-shot are small and dataset-dependent, and several rows get worse — the extreme is MOIRAI-1.1-R-large full fine-tuning on BPI2019-1 (23.06 vs 12.30 zero-shot). This is the per-number evidence behind "skip fine-tuning at PMF data scale."

+`,raw:"",frontmatter:De,filepath:"",start:1404,id:23,no:24},__clicksContext:null,__preloaded:!1}),ti={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"ER reaches parity on three logs — Sepsis is the exception"},Ne=T(ti),ni=A({get layout(){return Ne.layout},get transition(){return Ne.transition},get class(){return Ne.class},get clicks(){return Ne.clicks},get name(){return Ne.name},get preload(){return Ne.preload},slide:{content:"",revision:"-5ad2ks",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: ER reaches parity on three logs — Sepsis is the exception +`,note:"",index:24,noteHTML:`

speaker note: The main ER slide showed only Hospital Billing; this generalizes it. On three of four logs TSFMs sit at parity with the baselines. Sepsis is the lone exception — it is both heterogeneous (many rare variants) and intrinsically hard to encode, so ER stays high. This sets up the next Sepsis-specific frame.

+`,raw:"",frontmatter:Ne,filepath:"",start:1420,id:24,no:25},__clicksContext:null,__preloaded:!1}),si={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"Baselines were tuned; the fine-tuning recipe is fully specified"},qe=T(si),oi=A({get layout(){return qe.layout},get transition(){return qe.transition},get class(){return qe.class},get clicks(){return qe.clicks},get name(){return qe.name},get preload(){return qe.preload},slide:{content:"",revision:"-cuxszr",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: Baselines were tuned; the fine-tuning recipe is fully specified +`,note:"",index:25,noteHTML:`

speaker note: Pre-empts "was the baseline even tuned?" — yes, XGBoost gets per-dataset Optuna search over lagged daily-aggregated features, so the comparison is fair. And "what was the LoRA setup?" — rank 2, alpha 4 on the attention projections (Q/K/V/O), patch 16, batch 32, AdamW at 1e-4 for 3 epochs; full fine-tuning instead follows each model's own published recipe. Deliberately not quoting an Optuna trial count: the exact N isn't pinned in the repo, so I won't invent one — happy to confirm offline.

+`,raw:"",frontmatter:qe,filepath:"",start:1436,id:25,no:26},__clicksContext:null,__preloaded:!1}),ri={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"DF series are hard on transition, shifting, non-Gaussianity"},xe=T(ri),ai=A({get layout(){return xe.layout},get transition(){return xe.transition},get class(){return xe.class},get clicks(){return xe.clicks},get name(){return xe.name},get preload(){return xe.preload},slide:{content:"",revision:"-ld1we9",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: DF series are hard on transition, shifting, non-Gaussianity +`,note:"",index:26,noteHTML:`

speaker note: The full seven-metric profile — seasonality, trend, stationarity, transition, shifting, correlation, non-Gaussianity — backs the "DF series are unusually hard" claim. Three metrics stand out: frequent regime transitions, distribution shifting over time, and strong non-Gaussianity. The comparison to public forecasting benchmarks stays qualitative: we have no benchmark bars in the repo, so claim only that these three metrics run higher than typical public benchmarks (Li et al. 2025).

+`,raw:"",frontmatter:xe,filepath:"",start:1466,id:26,no:27},__clicksContext:null,__preloaded:!1}),ii={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"Sepsis is an ER exception — extreme behavioral heterogeneity"},Fe=T(ii),li=A({get layout(){return Fe.layout},get transition(){return Fe.transition},get class(){return Fe.class},get clicks(){return Fe.clicks},get name(){return Fe.name},get preload(){return Fe.preload},slide:{content:"",revision:"-t3jpue",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: Sepsis is an ER exception — extreme behavioral heterogeneity +`,note:"",index:27,noteHTML:`

speaker note: Sepsis is the ER outlier. 790 variants over 999 cases means an enormous long tail and very sparse DF activity per relation — so the forecasted model encodes traces poorly and fitting ratios collapse (TSFMs 4–18%). High heterogeneity (variant tail + sparse DF) drives the tiny ratios. Crucially this is ER only: on MAE, Sepsis is still a win — MOIRAI-2.0 cuts error 28%. Don't conflate the two metrics. Sources: stats_log.tex, results_ER.tex, results_1_mae.tex.

+`,raw:"",frontmatter:Fe,filepath:"",start:1485,id:27,no:28},__clicksContext:null,__preloaded:!1}),ci={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"We forecast a 7-day horizon; longer horizons are future work"},He=T(ci),ui=A({get layout(){return He.layout},get transition(){return He.transition},get class(){return He.class},get clicks(){return He.clicks},get name(){return He.name},get preload(){return He.preload},slide:{content:"",revision:"45r7kq",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: We forecast a 7-day horizon; longer horizons are future work +`,note:"",index:28,noteHTML:`

speaker note: 7 days is a deliberate choice, not a limitation we hide — it lines up with the Yu 2025 benchmark, the logs' own reporting cadence, and typical TSFM pretraining horizons, so comparisons are clean. We did NOT test month-long horizons; I'd flag that openly as future work, ideally paired with drift detection so a forecast is refreshed when the process shifts rather than extrapolated blindly.

+`,raw:"",frontmatter:He,filepath:"",start:1518,id:28,no:29},__clicksContext:null,__preloaded:!1}),fi={layout:"assertion-evidence",class:"backup",hideInToc:!0,locator:"Backup",assertion:"Multivariate gave no consistent gains over univariate"},je=T(fi),hi=A({get layout(){return je.layout},get transition(){return je.transition},get class(){return je.class},get clicks(){return je.clicks},get name(){return je.name},get preload(){return je.preload},slide:{content:"",revision:"-x89ixn",frontmatterRaw:`layout: assertion-evidence +class: backup +hideInToc: true +locator: Backup +assertion: Multivariate gave no consistent gains over univariate +`,note:"",index:29,noteHTML:`

speaker note: This was pre-empted on the DF-complexity slide — DF series are highly heterogeneous, so the cross-series attention that helps homogeneous panels does not transfer. Here it's reinforced with the actual experiment: we ran true multivariate inference on MOIRAI, MOIRAI-MoE, and Chronos-2 (all three support it natively), and saw no consistent improvement over univariate, matching Yu 2025. We don't claim multivariate is hopeless — only that off-the-shelf shared attention doesn't help at this scale; dedicated multivariate fine-tuning for PMF is the open direction.

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o=e.placement;if(!o&&t)for(const i of Lt)t[i]&&(o=i);return o||(o=he(e.theme||"tooltip","placement")),o}function Ft(e,t,o){let i;const n=typeof t;return n==="string"?i={content:t}:t&&n==="object"?i=t:i={content:!1},i.placement=Qo(i,o),i.targetNodes=()=>[e],i.referenceNode=()=>e,i}let Ne,ue,Jo=0;function ei(){if(Ne)return;ue=He([]),Ne=Gt({name:"VTooltipDirectiveApp",setup(){return{directives:ue}},render(){return this.directives.map(t=>Yt(Zo,{...t.options,shown:t.shown||t.options.shown,key:t.id}))},devtools:{hide:!0}});const e=document.createElement("div");document.body.appendChild(e),Ne.mount(e)}function ti(e,t,o){ei();const i=He(Ft(e,t,o)),n=He(!1),s={id:Jo++,options:i,shown:n};return ue.value.push(s),e.classList&&e.classList.add(Dt),e.$_popper={options:i,item:s,show(){n.value=!0},hide(){n.value=!1}}}function It(e){if(e.$_popper){const t=ue.value.indexOf(e.$_popper.item);t!==-1&&ue.value.splice(t,1),delete e.$_popper,delete e.$_popperOldShown,delete 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s}}e.use(si,{...t,themes:{...t.themes,twoslash:{$extend:"dropdown",triggers:Me?["touch"]:["hover","touch"],popperTriggers:Me?["touch"]:["hover","touch"],placement:"bottom-start",overflowPadding:10,delay:0,handleResize:!1,autoHide:!0,noAutoFocus:!0,instantMove:!0,flip:!1,arrowPadding:8,autoBoundaryMaxSize:!0,...t.themes?.twoslash??{}},"twoslash-query":{$extend:"twoslash",triggers:["click"],popperTriggers:["click"],autoHide:!1,noAutoFocus:!0,...t.themes?.["twoslash-query"]??{}},"twoslash-completion":{$extend:"twoslash-query",triggers:["click"],popperTriggers:["click"],autoHide:!1,noAutoFocus:!0,distance:0,arrowOverflow:!0,...t.themes?.["twoslash-completion"]??{}}}})}};export{ai as L,di as T,li as V,De as t}; diff --git a/assets/modules/shiki-UqVAgQbi.css b/assets/modules/shiki-UqVAgQbi.css new file mode 100644 index 0000000000000000000000000000000000000000..2fb42e950615def1c1d47147a88ca77cc5e9db2d --- /dev/null +++ b/assets/modules/shiki-UqVAgQbi.css @@ -0,0 +1 @@ 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e=t,s=r(()=>e.as||(e.to?"router-link":"button"));return(a,y)=>(u(),l(c(s.value),{class:"slidev-icon-btn",title:t.title,to:t.to},{default:i(()=>[o("span",p,d(t.title),1),m(a.$slots,"default",{},()=>[o("div",{class:f(t.icon)},null,2)])]),_:3},8,["title","to"]))}});export{B as _}; diff --git a/assets/slidev/Modal.vue_vue_type_script_setup_true_lang-CkGcf2ff.js b/assets/slidev/Modal.vue_vue_type_script_setup_true_lang-CkGcf2ff.js new file mode 100644 index 0000000000000000000000000000000000000000..c7ed6ffdd7241eadfd5a9cf5af1c1976cdbd2b21 --- /dev/null +++ b/assets/slidev/Modal.vue_vue_type_script_setup_true_lang-CkGcf2ff.js @@ -0,0 +1 @@ +import{f as r,b as m,T as c,e as u,g as t,i as f,n as p,j as b,at as k,ah as v,o as s}from"../modules/vue-BhY7Gbqw.js";const g={key:0,class:"fixed top-0 bottom-0 left-0 right-0 grid z-modal"},h=r({__name:"Modal",props:{modelValue:{default:!1},class:{default:""}},emits:["update:modelValue"],setup(l,{emit:n}){const e=l,a=v(e,"modelValue",n);function d(){a.value=!1}return(i,o)=>(s(),m(k,null,[c(a)?(s(),u("div",g,[t("div",{bg:"black opacity-80",class:"absolute top-0 bottom-0 left-0 right-0 -z-1",onClick:o[0]||(o[0]=_=>d())}),t("div",{class:p(["m-auto rounded-md bg-main shadow",e.class]),"dark:border":"~ main"},[f(i.$slots,"default")],2)])):b("v-if",!0)],1024))}});export{h as _}; diff --git a/assets/slidev/NoteDisplay.vue_vue_type_style_index_0_lang-DBgBQFXW.js b/assets/slidev/NoteDisplay.vue_vue_type_style_index_0_lang-DBgBQFXW.js new file mode 100644 index 0000000000000000000000000000000000000000..309a41dcee4d09d3394c60c77c1d719abccd5545 --- /dev/null +++ b/assets/slidev/NoteDisplay.vue_vue_type_style_index_0_lang-DBgBQFXW.js @@ -0,0 +1 @@ +import{f as B,e as p,n as m,g as c,T as H,F as $,t as y,aj as T,af as N,ag as z,k as E,z as h,o as f,K as I,L as F,E as P,r as A,p as K}from"../modules/vue-BhY7Gbqw.js";import{C as D,r as R,v as V,_ as j}from"../index-BPLLizhg.js";const q=["title"],U={class:"flex gap-0.2 items-center min-w-16 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C(){if(!n.value||!v.value)return;const k=Array.from(n.value.querySelectorAll(`.${g}`)),r=new Map,l=new Map;let M=0;for(const a of k){const s=Number(a.dataset.clicks);r.set(a,s);let o=a,e=a.parentElement;for(;e&&o!==n.value;)l.has(e)||l.set(e,[[null,M]]),l.get(e).push([o,s]),o=e,e=e.parentElement;M=s}const S=new Map;for(const[a,s]of l){let o=!1,e=0;for(const i of Array.from(a.childNodes)){let w=!1;for(;i===s[e+1]?.[0];)w=!0,e++;if(w)continue;let _=i;if(i.nodeType===3){if(!i.textContent?.trim())continue;_=document.createElement("span"),_.textContent=i.textContent,a.insertBefore(_,i),i.remove()}o||=e===0,S.set(_,s[e][1])}o||(s[0][1]=-1)}return a=>{const s=t.highlight;for(const[o,e]of l)o.classList.toggle(L,s&&!e.some(([i,w])=>w===a));for(const[o,e]of S)o.classList.toggle(L,s&&e!==a);for(const[o,e]of r)o.classList.remove(L),o.classList.toggle(`${g}-past`,s&&ea+1),o.ondblclick=i=>{s&&(x("markerDblclick",i,e),!i.defaultPrevented&&(t.clicksContext.current=e,i.stopPropagation(),i.stopImmediatePropagation()))},o.onclick=i=>{s&&x("markerClick",i,e)},s&&t.autoScroll&&e===a&&o.scrollIntoView({block:"center",behavior:"smooth"})}}const b=A();return I(()=>[t.noteHtml,t.highlight],()=>{K(()=>{b.value=C()})},{immediate:!0}),F(()=>{C()}),P(()=>{const k=t.clicksContext?.current??D;b.value?.(k)}),(k,r)=>u.noteHtml?(f(),p("div",{key:0,ref_key:"noteDisplay",ref:n,class:m(["prose dark:prose-invert overflow-auto outline-none slidev-note",[t.class,v.value?"slidev-note-with-clicks":""]]),innerHTML:u.noteHtml},null,10,Y)):u.note?(f(),p("div",{key:1,class:m(["prose dark:prose-invert overflow-auto outline-none slidev-note",t.class])},[c("p",{textContent:y(u.note)},null,8,Z)],2)):(f(),p("div",{key:2,class:m(["prose dark:prose-invert overflow-auto outline-none opacity-50 italic select-none slidev-note",t.class])},[c("p",{textContent:y(t.placeholder||"No notes.")},null,8,ee)],2))}});export{le as C,se as _}; diff --git a/assets/slidev/SlideWrapper-d1IDGvy1.js b/assets/slidev/SlideWrapper-d1IDGvy1.js new file mode 100644 index 0000000000000000000000000000000000000000..52c98229ef22388316021ea4f23965d13be24f7b --- /dev/null +++ b/assets/slidev/SlideWrapper-d1IDGvy1.js @@ -0,0 +1 @@ +import{a1 as Vt,W as ct,aI as qt,y as Ht,p as Zt,K as Yt,r as L,z as C,P as Nt,f as mt,T as Y,o as X,e as et,j as At,aJ as Xt,E as Jt,O as Qt,aK as A,k as tt,g as V,i as _t,F as Wt,h as rt,ai as te,n as ee,d as St,b as se,ay as ne}from"../modules/vue-BhY7Gbqw.js";import{a as Ft,a5 as dt,a6 as wt,a4 as re,o as ft,a7 as ie,a8 as R,a9 as oe,aa as ae,w as le,Y as he,ab as ue,M as bt,V as Pt,W as Et,ac as Mt,b as ce,_ as jt,ad as de,ae as fe,af as pe,ag as ge,ah as me,ai as ve,aj as ye}from"../index-BPLLizhg.js";let xe=()=>({emit(t,...e){for(let 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V={class:"relative flex gap-px"},G=w({__name:"CurrentProgressBar",props:{clicksContext:{},current:{}},setup(r){const e=r,a=q(),t=s(()=>e.clicksContext??a.clicksContext.value),l=s(()=>e.current??a.currentSlideNo.value),{total:n}=a;return(d,g)=>(o(),u("div",V,[(o(!0),u($,null,M(x(n)-1,i=>(o(),u("div",{key:i,class:A(["border-x border-b border-main h-4px transition-all",i[i===l.value?(o(),u("div",{key:0,class:"h-full bg-primary op75 transition-all",style:f({width:`${t.value.total===0?0:t.value.current/(t.value.total+1)*100}%`})},null,4)):m("v-if",!0)]),_:2},1024)],6))),128))]))}});function E(){const 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b/assets/slidev/context-C7ShjSuS.js new file mode 100644 index 0000000000000000000000000000000000000000..f072697ab9f20e53793b3d0b8ed329854b4a37c5 --- /dev/null +++ b/assets/slidev/context-C7ShjSuS.js @@ -0,0 +1 @@ +import{a8 as t,z as d,r as j,ai as a,aN as $}from"../modules/vue-BhY7Gbqw.js";import{ah as f,ai as C,Y as x,ae as S,ag as p,ad as v,af as R,al as T,am as E,an as F}from"../index-BPLLizhg.js";function A(){const n=t(T),o=a(n,"nav"),e=t(f).value,s=a(e,"current"),i=t(R),c=t(p),r=t(S,{}),l=t(v,void 0),u=t(x,j(1)),m=t(C,d(()=>1));return{$slidev:n,$nav:o,$clicksContext:e,$clicks:s,$page:i,$route:l,$renderContext:c,$frontmatter:r,$scale:u,$zoom:m}}function D(n,o){return{...$(n,o===0?E:F),frontmatter:n}}export{D as f,A as u}; diff --git a/assets/slidev/entry-DUHwO2Cm.js b/assets/slidev/entry-DUHwO2Cm.js new file mode 100644 index 0000000000000000000000000000000000000000..60627eadf0adc54ed1353b170076005a31eff987 --- /dev/null +++ b/assets/slidev/entry-DUHwO2Cm.js @@ -0,0 +1 @@ +import{e as n,d as t,w as o,a as r,o as i,g as l,ad as a}from"../modules/vue-BhY7Gbqw.js";import{_ as d}from"../index-BPLLizhg.js";import"../modules/shiki-BIEePiOi.js";const c={},f={class:"h-full w-full flex items-center justify-center gap-5 lt-md:flex-col"};function p(u,e){const s=r("RouterLink");return i(),n("div",f,[t(s,{to:"/",class:"page-link"},{default:o(()=>[...e[0]||(e[0]=[l("div",{class:"i-carbon:presentation-file"},null,-1),a(" Slides ",-1)])]),_:1}),t(s,{to:"/presenter",class:"page-link"},{default:o(()=>[...e[1]||(e[1]=[l("div",{class:"i-carbon:user-speaker"},null,-1),a(" Presenter ",-1)])]),_:1}),t(s,{to:"/notes",class:"page-link"},{default:o(()=>[...e[2]||(e[2]=[l("div",{class:"i-carbon:catalog"},null,-1),a(" Notes ",-1)])]),_:1}),t(s,{to:"/overview",class:"page-link"},{default:o(()=>[...e[3]||(e[3]=[l("div",{class:"i-carbon:list-boxes"},null,-1),a(" Overview ",-1)])]),_:1})])}const k=d(c,[["render",p],["__scopeId","data-v-33d72d6e"]]);export{k as default}; diff --git 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0000000000000000000000000000000000000000..57a569b49a9e08e472c326a72c1cb03b6dec0cb4 --- /dev/null +++ b/figures/README.md @@ -0,0 +1,36 @@ +# Slidev figure assets + +Served by Slidev at root: reference in `slides.md` as ``. + +Semantic kebab-case naming. Full per-beat mapping in +`slides/talk_design/workflow.md` (Figure naming convention). + +## Currently expected (drop files here as you produce them) + +| File | Used by | Notes | +|---|---|---| +| `dfg-evolution.gif` (or `.mp4`) | beat 2 | DFG t₁→t₂→t₃→forecasted t₄ animation, 10–15s | +| `bpi2017-drift-xgb-only.png` | beat 3 | Ground truth + XGBoost only — TSFM line stripped for callback | +| `tsfm-release-timeline.svg` | beat 6 | 2023→2026 horizontal timeline, 3 family lanes, dots per version | +| `results-mae-bars.png` | beat 7a | 4 panels, 5 horizontal bars per panel, baselines gray + TSFMs accent | +| `bpi2017-drift-with-tsfm.png` | beat 7b | Full reveal — actual + XGBoost + TSFM (paper Fig 1 BPI2017 panel) | +| `ft-slope.png` | beat 8 | 3-point slope ZS→LoRA→Full-FT, one line per (model × dataset), colored by dataset | +| `ft-compute-bars.png` | beat 8 | Log-scale wall-clock bars, 3 ZS + 3 LoRA + 3 Full-FT + 1 XGBoost | +| `er-bars.png` | beat 9 | Compact 4-panel ER bars, 3 TSFMs + 2 baselines, Truth/Training reference lines | +| `demo-screencast.mp4` | beat 11 | ~2 min pre-recorded screencast: load log → pick TSFM → render forecasted DFG | +| `baseline-ranking-full.png` | backup | Full benchmark ranking from Yu 2025 | +| `rmse-full.png` | backup | Full RMSE table | +| `df-complexity-radar.png` | backup | 7-metric DF complexity vs 21 standard benchmarks | +| `sepsis-variants.png` | backup | Sepsis trace variant distribution / DFG showing heterogeneity | + +When a file lands here, the next `/03-content-pass ` invocation auto-wires it +into the slide (replaces the placeholder `
` with ``). + +## Source pipeline + +- For the 4 plots derived from existing `.eps` in `slides/figures/`: + convert with `pdftocairo -png -r 300 source.eps target` and rename per the table above. +- For new charts (results bars, slope, compute, ER): generate from your experiment outputs + with matplotlib; save as PNG at 2× resolution. +- For animations: Manim or matplotlib + ffmpeg. +- For the screencast: any screen-recording tool; export at 720p+, ≤2 min. diff --git a/figures/_preview-mae-bars-v2.png b/figures/_preview-mae-bars-v2.png new file mode 100644 index 0000000000000000000000000000000000000000..94b80e142cb533e253c8e4b5b2b98580b764f159 Binary files /dev/null and b/figures/_preview-mae-bars-v2.png differ diff --git a/figures/_preview-mae-bars.png b/figures/_preview-mae-bars.png new file mode 100644 index 0000000000000000000000000000000000000000..07a933ad2aad79fe19e2aad8cac0d29fd6bed90f Binary files /dev/null and b/figures/_preview-mae-bars.png differ diff --git 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-

Welcome to your static Space!

-

You can modify this app directly by editing index.html in the Files and versions tab.

-

- Also don't forget to check the - Spaces documentation. -

-
- + + + + + +Time Series Foundation Models for Process Model Forecasting - Slidev + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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