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Deploy CAiSE 2026 slides (final deck)
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.gitattributes
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>Time Series Foundation Models for Process Model Forecasting - Slidev</title>
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<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500;600;700&display=swap" type="text/css">
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<meta property="slidev:version" content="52.14.1">
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<meta name="description" content=""CAiSE 2026 presentation — pmf-tsfm\nYongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt\nKU Leuven · LIRIS\n"">
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<meta property="og:title" content="Time Series Foundation Models for Process Model Forecasting - Slidev">
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<meta property="og:description" content=""CAiSE 2026 presentation — pmf-tsfm\nYongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt\nKU Leuven · LIRIS\n""> <script type="module" crossorigin src="/assets/index-BPLLizhg.js"></script>
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README.md
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---
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title:
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emoji: 📈
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title: PMF-TSFM CAiSE 2026 Slides
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emoji: 📈
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colorFrom: blue
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colorTo: yellow
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sdk: static
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---
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Live Slidev deck for the CAiSE 2026 talk — Process Model Forecasting with Time Series Foundation
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Models. Built from `slides/template` in github.com/YongboYu/pmf-tsfm.
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import{_ as u}from"./Callout-Drpvzfz7.js";import{aL as f,o as r,b as g,w as n,g as s,j as t,ad as e,af as i,e as d,d as c,v as _,x as k,T as p}from"./modules/vue-BhY7Gbqw.js";import{_ as w}from"./assertion-evidence-Bbax46Gu.js";import{u as x,f as b}from"./slidev/context-C7ShjSuS.js";import{_ as L}from"./index-BPLLizhg.js";import"./PageNo-COvz-9-K.js";import"./modules/shiki-BIEePiOi.js";const T={class:"s8-evidence mt-4"},M={class:"s8-link s8-link--data"},C={class:"s8-link s8-link--task"},y={__name:"slides.md__slidev_8",setup(z){const{$clicksContext:m,$frontmatter:v}=x();return m.setup(),(B,a)=>{const l=u,o=f("click");return r(),g(w,_(k(p(b)(p(v),7))),{default:n(()=>[s("div",T,[a[6]||(a[6]=s("div",{class:"grid grid-cols-2 items-stretch",style:{gap:"186px"}},[t(" LEFT — LLM: foundation model for language (neutral gray) "),s("div",{class:"s8-card"},[s("div",{class:"s8-name"},"Large Language Model (LLM)"),s("div",{class:"s8-ex"},[s("span",{class:"s8-ex-q"},[e("I am a math "),s("span",{class:"s8-blank"},"?")]),s("span",{class:"s8-ex-arrow"},"→"),s("span",{class:"s8-ex-a"},"teacher")]),s("div",{class:"s8-rows"},[s("div",{class:"s8-row"},[s("span",{class:"s8-rk"},"training data"),s("span",{class:"s8-rv"},"web-scale text")]),s("div",{class:"s8-row"},[s("span",{class:"s8-rk"},"tasks"),s("span",{class:"s8-rv"},"language")])])]),t(" RIGHT — TSFM: foundation model for time series, what we use (brand-navy emphasis) "),s("div",{class:"s8-card s8-card--tsfm"},[s("div",{class:"s8-badge"},"what we use"),s("div",{class:"s8-name s8-name--brand"},"Time Series Foundation Model (TSFM)"),s("div",{class:"s8-ex"},[s("span",{class:"s8-ex-q"},[e("…12, 9, 14, 11, "),s("span",{class:"s8-blank"},"?")]),s("span",{class:"s8-ex-arrow"},"→"),s("span",{class:"s8-ex-a s8-ex-a--brand"},"13")]),s("div",{class:"s8-rows"},[s("div",{class:"s8-row"},[s("span",{class:"s8-rk"},"training data"),s("span",{class:"s8-rv s8-rv--brand"},"millions of diverse series")]),s("div",{class:"s8-row"},[s("span",{class:"s8-rk"},"tasks"),s("span",{class:"s8-rv s8-rv--brand"},"forecasting")])])])],-1)),t(" centered linking callouts (double arrows) bridging the two shared rows; reveal on click "),i((r(),d("div",M,[a[1]||(a[1]=s("span",{class:"s8-larr"},"←",-1)),c(l,null,{default:n(()=>[...a[0]||(a[0]=[e("pretrained at scale",-1)])]),_:1}),a[2]||(a[2]=s("span",{class:"s8-rarr"},"→",-1))])),[[o,1]]),i((r(),d("div",C,[a[4]||(a[4]=s("span",{class:"s8-larr"},"←",-1)),c(l,null,{default:n(()=>[...a[3]||(a[3]=[e("generalizable",-1)])]),_:1}),a[5]||(a[5]=s("span",{class:"s8-rarr"},"→",-1))])),[[o,1]])]),a[7]||(a[7]=s("div",{class:"s8-defs"},[s("div",{class:"s8-def"},[s("strong",null,"zero-shot"),e(": frozen parameters, run as-is")]),s("div",{class:"s8-def"},[s("strong",null,"fine-tuning"),e(": tweak the parameters on your own data")])],-1))]),_:1},16)}}},q=L(y,[["__scopeId","data-v-1cd822ed"]]);export{q as default};
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import{o,b as i,w as r,g as e,v as n,x as c,T as l}from"./modules/vue-BhY7Gbqw.js";import{_ as u}from"./assertion-evidence-Bbax46Gu.js";import{u as d,f as p}from"./slidev/context-C7ShjSuS.js";import"./PageNo-COvz-9-K.js";import"./index-BPLLizhg.js";import"./modules/shiki-BIEePiOi.js";const B={__name:"slides.md__slidev_26",setup(m){const{$clicksContext:t,$frontmatter:s}=d();return t.setup(),(f,a)=>(o(),i(u,n(c(l(p)(l(s),25))),{default:r(()=>[...a[0]||(a[0]=[e("div",{class:"ae-cols"},[e("div",{class:"ae-col"},[e("div",{class:"ae-collabel"},"XGBoost"),e("div",{class:"ae-lead"},"Tuned per dataset, not left at defaults"),e("ul",null,[e("li",null,"Optuna hyperparameter optimization, per dataset"),e("li",null,"Lagged features, daily aggregation")])]),e("div",{class:"ae-col"},[e("div",{class:"ae-collabel"},"LoRA / full fine-tuning"),e("div",{class:"ae-lead"},"Fully specified, reproducible recipe"),e("ul",null,[e("li",null,"LoRA r=2, α=4 on Q/K/V/O attention"),e("li",null,"Patch 16 · batch 32 · AdamW · LR 1e-4 · 3 epochs"),e("li",null,"Full fine-tuning uses each model's original recipe")])])],-1)])]),_:1},16))}};export{B as default};
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import{o as i,b as n,w as d,g as s,j as e,ad as a,v as c,x as p,T as r}from"./modules/vue-BhY7Gbqw.js";import{_ as m}from"./assertion-evidence-Bbax46Gu.js";import{u,f}from"./slidev/context-C7ShjSuS.js";import{_ as v}from"./index-BPLLizhg.js";import"./PageNo-COvz-9-K.js";import"./modules/shiki-BIEePiOi.js";const _={__name:"slides.md__slidev_9",setup(b){const{$clicksContext:t,$frontmatter:l}=u();return t.setup(),(g,o)=>(i(),n(m,c(p(r(f)(r(l),8))),{default:d(()=>[...o[0]||(o[0]=[s("div",{class:"s9-flow"},[e(' ROW 1 — our data: complex + short (number dropped; "small" sharpened to short series → few samples) '),s("div",{class:"s9-row"},[s("div",{class:"s9-tag"},"our data"),s("div",{class:"s9-line"},[s("div",{class:"s9-sub"},[s("span",{class:"s9-lead"},[a("complex signal · "),s("strong",{class:"s9-em"},"short series (few samples)")])]),s("div",{class:"s9-sub"},[s("span",{class:"s9-arr"},"→"),s("span",{class:"s9-out s9-out--bad"},[a("from-scratch models "),s("strong",null,"overfit")])])])]),e(" pivot connector "),s("div",{class:"s9-so"},"so"),e(" THE BET — the focal claim: a foundation model brings a prior "),s("div",{class:"s9-row s9-row--bet"},[s("div",{class:"s9-tag s9-tag--brand"},"the bet"),s("div",{class:"s9-line"},[s("div",{class:"s9-sub"},[s("span",{class:"s9-lead s9-lead--brand"},"pretrained on"),s("span",{class:"s9-chip s9-chip--big"},"millions of diverse series")]),s("div",{class:"s9-sub"},[s("span",{class:"s9-arr s9-arr--brand"},"→"),s("span",{class:"s9-out s9-out--good"},[a("a "),s("strong",null,"broad prior"),a(", not a fresh fit")])])])])],-1),s("div",{class:"s9-caveat"},[a("No event logs in pretraining, "),s("em",null,"to our knowledge"),a(".")],-1)])]),_:1},16))}},C=v(_,[["__scopeId","data-v-d87e63f4"]]);export{C as default};
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import{o as n,e as i,g as s,i as d,b as u,w as _,ad as e,v as m,x as f,T as o}from"./modules/vue-BhY7Gbqw.js";import{_ as c}from"./index-BPLLizhg.js";import{u as p,f as v}from"./slidev/context-C7ShjSuS.js";import"./modules/shiki-BIEePiOi.js";const g={},x={class:"slidev-layout center h-full grid place-content-center"},h={class:"my-auto"};function b(t,a){return n(),i("div",x,[s("div",h,[d(t.$slots,"default")])])}const k=c(g,[["render",b]]),q={__name:"slides.md__slidev_10",setup(t){const{$clicksContext:a,$frontmatter:l}=p();return a.setup(),(T,r)=>(n(),u(k,m(f(o(v)(o(l),9))),{default:_(()=>[...r[0]||(r[0]=[s("div",{class:"s10"},[s("div",{class:"s10-heading"},"Three questions this talk answers"),s("div",{class:"s10-rule"}),s("div",{class:"s10-list"},[s("div",{class:"s10-q"},[s("span",{class:"s10-num"},"1"),s("div",{class:"s10-text"},[e("Can "),s("strong",null,"zero-shot"),e(" TSFMs give better "),s("strong",null,"DF time-series forecasts"),e("?")])]),s("div",{class:"s10-q"},[s("span",{class:"s10-num"},"2"),s("div",{class:"s10-text"},[e("Does "),s("strong",null,"fine-tuning"),e(" improve the results further?")])]),s("div",{class:"s10-q s10-q--open"},[s("span",{class:"s10-num s10-num--amber"},"3"),s("div",{class:"s10-text"},[e("Does a better forecast give us a "),s("strong",null,"better forecasted process model"),e("?")])])])],-1)])]),_:1},16))}},y=c(q,[["__scopeId","data-v-6335c26f"]]);export{y as default};
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import{_ as p}from"./Callout-Drpvzfz7.js";import{aL as m,o as a,b as _,w as r,g as s,j as o,ad as t,af as f,e as u,d as v,v as g,x as b,T as i}from"./modules/vue-BhY7Gbqw.js";import{_ as x}from"./assertion-evidence-Bbax46Gu.js";import{u as F,f as h}from"./slidev/context-C7ShjSuS.js";import{_ as k}from"./index-BPLLizhg.js";import"./PageNo-COvz-9-K.js";import"./modules/shiki-BIEePiOi.js";const w={class:"s18"},y={class:"flex justify-center mt-3 s18-cta"},T={__name:"slides.md__slidev_18",setup(C){const{$clicksContext:l,$frontmatter:n}=F();return l.setup(),(M,e)=>{const c=p,d=m("click");return a(),_(x,g(b(i(h)(i(n),17))),{default:r(()=>[s("div",w,[o(" For Process Model Forecasting — the two PMF-specific signals, boxed (four-logs hedge stays spoken) "),e[1]||(e[1]=s("div",{class:"s18-prac s18-pmf"},[s("div",{class:"s18-prac-label"},"For process mining"),s("div",{class:"s18-pmf-rows"},[s("div",{class:"s18-pmf-row"},[s("strong",null,"Zero-shot"),t(" is the new default of PMF · "),s("strong",null,"Fine-tuning is marginal"),t(" at this data scale")])])],-1)),o(" For practitioners — applied guidance (brand-tinted banner) "),e[2]||(e[2]=s("div",{class:"s18-prac"},[s("div",{class:"s18-prac-label"},"For practitioners"),s("div",{class:"s18-prac-body"},[t(" These TSFMs are "),s("strong",null,"tiny beside LLMs"),t(": deploy locally, forecast fast on a laptop, with little compute. ")])],-1)),o(" General CTA — the broadening close, the one amber Callout, on click "),f((a(),u("div",y,[v(c,null,{default:r(()=>[...e[0]||(e[0]=[t("Try TSFMs for your forecasts",-1)])]),_:1})])),[[d]])])]),_:1},16)}}},j=k(T,[["__scopeId","data-v-f1a3d347"]]);export{j as default};
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import{_ as u}from"./Callout-Drpvzfz7.js";import{aL as f,o as s,b as _,w as r,g as e,ad as o,af as i,e as n,d as c,v,x as g,T as d}from"./modules/vue-BhY7Gbqw.js";import{_ as x}from"./assertion-evidence-Bbax46Gu.js";import{u as b,f as h}from"./slidev/context-C7ShjSuS.js";import"./index-BPLLizhg.js";import"./modules/shiki-BIEePiOi.js";import"./PageNo-COvz-9-K.js";const w="/figures/er-hospital-billing.png",k={class:"relative"},y={class:"absolute",style:{left:"40%",top:"14%"}},T={class:"absolute",style:{left:"58%",top:"40%"}},D={__name:"slides.md__slidev_16",setup(B){const{$clicksContext:m,$frontmatter:p}=b();return m.setup(),(C,t)=>{const a=u,l=f("click");return s(),_(x,v(g(d(h)(d(p),15))),{default:r(()=>[t[3]||(t[3]=e("div",{class:"text-center mb-3",style:{"font-size":"19px","max-width":"1040px","margin-left":"auto","margin-right":"auto"}},[e("b",{style:{color:"var(--ink)"}},"Entropic Relevance (ER):"),o(),e("span",{style:{color:"var(--neutral)"}},"average bits to encode one log trace with the forecasted process model ·"),o(),e("b",{style:{color:"var(--brand)"}},"lower = better")],-1)),e("div",k,[t[2]||(t[2]=e("img",{src:w,class:"block mx-auto w-full max-h-[380px] object-contain rounded-lg",alt:"Hospital Billing ER — reusing the historical (Training) model is far worse than any forecast; the five forecasts cluster close together"},null,-1)),i((s(),n("div",y,[c(a,{dir:"left"},{default:r(()=>[...t[0]||(t[0]=[o("Forecast ≫ reuse",-1)])]),_:1})])),[[l,1]]),i((s(),n("div",T,[c(a,{dir:"down"},{default:r(()=>[...t[1]||(t[1]=[o("TSFMs ≈ baselines",-1)])]),_:1})])),[[l,2]])]),t[4]||(t[4]=e("div",{class:"caption text-center mt-2",style:{color:"var(--neutral)"}},"Hospital Billing · Truth = ideal floor · Training = reuse the historical model",-1))]),_:1},16)}}};export{D as default};
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import{aL as d,o as c,b as m,w as p,g as s,j as r,af as i,ad as o,v as _,x as f,T as a}from"./modules/vue-BhY7Gbqw.js";import{_ as v}from"./assertion-evidence-Bbax46Gu.js";import{u as g,f as u}from"./slidev/context-C7ShjSuS.js";import{_ as h}from"./index-BPLLizhg.js";import"./PageNo-COvz-9-K.js";import"./modules/shiki-BIEePiOi.js";const x="/figures/s6-drift-xgb.png",I="/figures/s14-drift-tsfm.png",b="/figures/s6-intermittent-xgb.png",B="/figures/s14-intermittent-tsfm.png",k={class:"grid grid-cols-2 gap-8 mt-3",style:{height:"330px"}},w={class:"flex flex-col"},y={class:"s14-stack"},P={src:x,class:"s14-img",alt:"BPI2017 drift — XGBoost stays high while truth collapses late"},R={src:I,class:"s14-img s14-img--over",alt:"BPI2017 drift revealed — MOIRAI-2.0 dips toward the truth in the boxed back-half where XGBoost stays elevated"},z={class:"flex flex-col"},C={class:"s14-stack"},O={src:b,class:"s14-img",alt:"BPI2019-1 intermittent — XGBoost hallucinates 40–71 where truth is mostly zero"},T={src:B,class:"s14-img s14-img--over",alt:"BPI2019-1 intermittent revealed — MOIRAI-2.0 holds near zero (amber arrow), no false bursts, no spikes"},A={__name:"slides.md__slidev_14",setup(D){const{$clicksContext:l,$frontmatter:n}=g();return l.setup(),(G,t)=>{const e=d("click");return c(),m(v,_(f(a(u)(a(n),13))),{default:p(()=>[s("div",k,[r(" LEFT — drift: reveal MOIRAI-2.0 + amber adaptation box "),s("div",w,[t[0]||(t[0]=s("div",{class:"s14-panel-label"},"Drift: tracked",-1)),s("div",y,[i(s("img",P,null,512),[[e,1,void 0,{hide:!0}]]),i(s("img",R,null,512),[[e,1]])]),t[1]||(t[1]=s("div",{class:"s14-cap"},"BPI2017 · Sent → Canceled",-1))]),r(" RIGHT — sparsity: reveal MOIRAI-2.0 + amber arrow to the zero line "),s("div",z,[t[2]||(t[2]=s("div",{class:"s14-panel-label"},"Sparsity: controlled",-1)),s("div",C,[i(s("img",O,null,512),[[e,1,void 0,{hide:!0}]]),i(s("img",T,null,512),[[e,1]])]),t[3]||(t[3]=s("div",{class:"s14-cap"},"BPI2019-1 · Cancel → Record Invoice Receipt",-1))])]),t[4]||(t[4]=s("div",{class:"mt-4 text-center",style:{"font-size":"20px",color:"#334155","line-height":"1.5"}},[s("div",null,[o("Drift: misses the drop, then "),s("strong",{style:{color:"var(--brand)"}},"adapts"),o(". Online context, no retraining.")]),s("div",null,[o("Sparsity: holds near zero. No false bursts, but "),s("strong",null,"no spikes either"),o(".")])],-1))]),_:1},16)}}},L=h(A,[["__scopeId","data-v-889dd224"]]);export{L as default};
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import{o as i,b as n,w as c,g as e,j as o,ad as t,v as d,x as p,T as l}from"./modules/vue-BhY7Gbqw.js";import{_ as m}from"./assertion-evidence-Bbax46Gu.js";import{u as f,f as u}from"./slidev/context-C7ShjSuS.js";import{_}from"./index-BPLLizhg.js";import"./PageNo-COvz-9-K.js";import"./modules/shiki-BIEePiOi.js";const g="/figures/s5-drift-truth.png",v="/figures/s5-intermittent-truth.png",x={__name:"slides.md__slidev_5",setup(h){const{$clicksContext:r,$frontmatter:a}=f();return r.setup(),(y,s)=>(i(),n(m,d(p(l(u)(l(a),4))),{default:c(()=>[...s[0]||(s[0]=[e("div",{class:"grid grid-cols-2 gap-8 mt-3",style:{height:"350px"}},[o(" LEFT — drift (truth only) "),e("div",{class:"flex flex-col"},[e("div",{class:"s5-panel-label"},"① Drift: a real level shift"),e("img",{src:g,class:"block w-full object-contain rounded-lg",style:{"max-height":"280px"},alt:"BPI2017 Sent→Canceled daily values — drops from ~46 to ~3 over the test period"}),e("div",{class:"s5-panel-cap"},"BPI2017 · Sent → Canceled")]),o(" RIGHT — intermittency (truth only) "),e("div",{class:"flex flex-col"},[e("div",{class:"s5-panel-label"},"② Intermittent: long zeros, sudden spikes"),e("img",{src:v,class:"block w-full object-contain rounded-lg",style:{"max-height":"280px"},alt:"BPI2019-1 Cancel→Record Invoice Receipt daily values — mostly zero with spikes to 29"}),e("div",{class:"s5-panel-cap"},"BPI2019-1 · Cancel → Record Invoice Receipt")])],-1),e("div",{class:"mt-4 text-center",style:{"font-size":"20px",color:"#334155"}},[t(" ③ "),e("strong",{style:{color:"var(--brand)"}},"Heterogeneous"),t(" · pattern differs "),e("strong",null,"within"),t(" and "),e("strong",null,"across"),t(" logs ")],-1)])]),_:1},16))}},w=_(x,[["__scopeId","data-v-3d8d3776"]]);export{w as default};
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