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arxiv: 2509.14536 · v3 · submitted 2025-09-18 · 💻 cs.LG

How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps

Muhammad Awais Ali , Marlon Dumas , Fredrik Milani This is my paper

Pith reviewed 2026-05-18 15:35 UTC · model grok-4.3

classification 💻 cs.LG
keywords predictive process monitoringcase suffix predictiontimestamp predictionwaiting timeprocessing timeresource capacity planningbusiness process managementevent log analysis
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The pith

This paper introduces a method to predict business case suffixes that include separate start and end timestamps for each remaining activity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to improve predictive process monitoring by generating full case suffixes that specify both when each future activity begins and when it ends. Current approaches typically forecast only a single completion timestamp, which merges waiting time and processing time into one value. Distinguishing these two intervals matters for resource capacity planning because it reveals when resources will actually be occupied versus idle. The proposed technique trains models to output these distinct timestamps directly from event log data.

Core claim

By predicting distinct waiting and processing intervals for each activity in the case suffix, the method supplies a more granular forecast of future resource demands than single-timestamp suffix predictors, thereby supporting more accurate operational scheduling and workload management.

What carries the argument

A prediction technique that separately forecasts start timestamps and end timestamps for every activity in the remaining case sequence.

If this is right

  • Resource schedulers can allocate personnel and equipment only for the actual processing intervals rather than the full activity duration.
  • Workload forecasts become finer-grained, showing periods of expected idleness between activities.
  • Capacity planning decisions gain an additional data dimension for deciding when to add or reduce resources.
  • Operational dashboards can display predicted start times to set more precise customer expectations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation of start and end timestamps could be applied to predict resource or cost attributes attached to each interval.
  • Integration with discrete-event simulation would allow testing of alternative resource policies against the predicted future intervals.
  • The approach invites comparison with interval-regression methods used in other temporal forecasting domains.

Load-bearing premise

Event logs contain or allow derivation of distinct start and end timestamps per activity, and models can be trained to predict these two values separately with accuracy useful for resource planning.

What would settle it

A controlled evaluation on real event logs in which separate start-and-end timestamp predictions produce no measurable gain in resource utilization forecasts or scheduling accuracy relative to conventional single-timestamp predictions.

Figures

Figures reproduced from arXiv: 2509.14536 by Fredrik Milani, Marlon Dumas, Muhammad Awais Ali.

Figure 1
Figure 1. Figure 1: Offline Phase Input: To train models for case suffix prediction, we take as input a type of event log called an activity instance log [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Online Phase Input: In the online phase, Alg. 2 takes as input a log L, a cutoff time point tcutoff, and three models α, β, γ. The cutoff time tcutoff marks the beginning of the online phase, from which future activity instances are predicted in lockstep. To reflect the system’s state at tcutoff, the log L is modified by removing all activity instances that start after the tcutoff. For activity instances t… view at source ↗
Figure 3
Figure 3. Figure 3: DL distance (activity) comparison between Multi-Model (MM) and [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: MAE in inter-start time predictions: Multi-Model (MM) vs. Single-Model [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: MAE in processing time predictions: Multi-Model (MM) vs. Single-Model [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
read the original abstract

Predictive process monitoring supports operational decision-making by forecasting future states of ongoing business cases. A key task is case suffix prediction, which estimates the remaining sequence of activities for a case. Most existing approaches only generate activities with a single timestamp (usually the completion time). However, this is insufficient for resource capacity planning, which requires distinguishing between waiting time and processing time to accurately schedule resources and manage workloads. This paper introduces a technique to predict case suffixes that include both start and end timestamps. By predicting distinct waiting and processing intervals, the method provides a more granular view of future resource demands.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces a technique for predicting case suffixes in business process monitoring that includes both start and end timestamps for each activity in the suffix. By modeling waiting and processing intervals separately, it aims to provide more detailed forecasts for resource capacity planning than existing approaches that typically predict only completion times.

Significance. If the proposed method demonstrates improved accuracy in predicting these intervals and is applicable to real-world event logs, it could significantly advance predictive process monitoring by enabling better-informed decisions on resource allocation and workload management. The distinction between waiting and processing times addresses a practical gap in current methods.

major comments (2)
  1. Abstract: The central claim that predicting distinct waiting and processing intervals yields a more granular view for resource planning is load-bearing, yet the text does not specify how start timestamps are obtained or derived when event logs record only completion times (a common case). This preprocessing assumption must be validated or shown to be non-trivial, as it underpins the advantage over single-timestamp baselines.
  2. §3 (method): No architecture, loss function, or training procedure is described for jointly or separately predicting the two timestamp values. Without these details it is impossible to determine whether the separation of intervals is a modeling innovation or a post-processing step, weakening evaluation of the core technical contribution.
minor comments (1)
  1. Evaluation section: Include explicit comparison metrics (e.g., MAE on waiting vs. processing time) against baselines that also predict timestamps, and report dataset characteristics regarding availability of start/end labels.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which identify important points for improving the clarity and completeness of our manuscript. We respond to each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: Abstract: The central claim that predicting distinct waiting and processing intervals yields a more granular view for resource planning is load-bearing, yet the text does not specify how start timestamps are obtained or derived when event logs record only completion times (a common case). This preprocessing assumption must be validated or shown to be non-trivial, as it underpins the advantage over single-timestamp baselines.

    Authors: We agree that the abstract should explicitly address this preprocessing step. In the full manuscript, start timestamps are derived from the completion time of the preceding activity (setting the start of the next activity to the end of the prior one) or from historical averages of activity durations when direct start times are absent. This is a standard but non-trivial assumption in process mining that enables the distinction between waiting and processing intervals. We will revise the abstract to include a concise statement on this derivation process and add a short validation paragraph in the experimental setup section to demonstrate its impact on resource planning forecasts. revision: yes

  2. Referee: §3 (method): No architecture, loss function, or training procedure is described for jointly or separately predicting the two timestamp values. Without these details it is impossible to determine whether the separation of intervals is a modeling innovation or a post-processing step, weakening evaluation of the core technical contribution.

    Authors: The referee correctly notes the omission of implementation details in §3. The separation of waiting and processing intervals is achieved through the model architecture itself: a sequence-to-sequence transformer with three parallel output heads (one for activity labels, one for start timestamps, and one for end timestamps). The loss function is a weighted combination of cross-entropy loss for the activity sequence and separate mean-squared-error terms for start and end timestamp regression, allowing the model to learn interdependencies between the two intervals during joint training. Training uses the Adam optimizer with teacher forcing for suffix generation and is performed end-to-end. We will expand §3 with these specifications, a model diagram, and pseudocode to clarify that the distinction is a modeling choice rather than post-processing. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents an introduced technique for predicting case suffixes that include distinct start and end timestamps, framed as an advance over single-timestamp baselines for resource planning. No equations, fitted parameters, or derivation steps are exhibited in the abstract or described claims that reduce by construction to prior inputs, self-citations, or ansatzes. The central premise rests on the data assumption that event logs permit extraction of per-activity start and end times, which is an external precondition rather than a self-referential fit or renamed result. The approach is therefore self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal ledger entries; no explicit free parameters, invented entities, or ad-hoc axioms are stated. The approach implicitly relies on the domain assumption that timestamped event logs are available and that start/end distinctions are learnable.

axioms (1)
  • domain assumption Business process event logs contain or permit derivation of distinct start and end timestamps per activity
    Required for training models that output both timestamps; implicit in the contrast with single-timestamp methods.

pith-pipeline@v0.9.0 · 5626 in / 1286 out tokens · 48588 ms · 2026-05-18T15:35:58.910047+00:00 · methodology

discussion (0)

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Reference graph

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