On the Potential of Multi-Task Learning in Predictive Process Monitoring

Lukas Kirchdorfer; Keyvan Amiri Elyasi; Heiner Stuckenschmidt

cs.LGSep 11, 2026

On the Potential of Multi-Task Learning in Predictive Process Monitoring

Authors: Lukas Kirchdorfer, Keyvan Amiri Elyasi, Heiner Stuckenschmidt

Organizations: University of Mannheim

Abstract

Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support toward proactive analysis and monitoring. Although deep learning has improved prediction accuracy in PPM, most approaches follow a single-task learning (STL) setup, training a separate model per task. This increases maintenance effort and overlooks potential synergies. Multi-task learning (MTL), which jointly learns multiple prediction targets in one model, offers a promising alternative, yet its effectiveness in PPM remains underexplored. It remains unclear whether and under which settings MTL improves upon STL, which prediction tasks benefit most from joint learning, which task combinations are particularly synergistic, and if and how tasks should be balanced. To fill this gap, we present the first comprehensive empirical study of MTL for PPM, evaluating a variety of task combinations, neural architectures, and optimization methods. Overall, our results position MTL as a strong paradigm for PPM: we see substantial improvements in next-activity prediction and inherent mitigation of class imbalance using MTL, while task balancing is especially critical under low-capacity models.

Figures & tables

	Tasks	Arch.	STL	Com.	MTO
Tax et al. (2017)	2	L	✓		EW
Gherissi et al. (2022)	3	L			EW
Camargo et al. (2019)	3	L	✓	$\sim$	EW
Taymouri et al. (2020)	2	L			EW
Hennig and Schmidt (2025)	3	T			EW
Wang et al. (2024)	3	T			static

Table 1: Comparison of existing PPM approaches that apply MTL. Arch.: L = LSTM, T = Transformer, C = CNN. STL indicates whether MTL is compared against its single-task baseline. Com. indicates whether all task combinations are evaluated. Analysis indicates whether the study analyzes when and why MTL is beneficial.

Panel of Figure 1 — Figure 1: Multi-task setup with shared backbone (LSTM/CNN/Transformer) and task-specific heads.

Event log	Cases	Events	ACT	RES	Variants	ALT
P2P	608	9119	21	27	70	21.5
Production	225	4503	24	41	217	20.6
Helpdesk	4580	21348	14	22	226	40.9
Sepsis	1050	15214	16	26	846	28.5
BPIC20DD	10500	56437	17	7	99	11.5
BPIC20ID	6449	72151	34	8	753	86.5

Table 2: Characteristics of the employed event logs: ACT = distinct activities, RES = distinct resources, ALT = average lead time of process instances (days).

Figure 2: MTL performance by model architecture, measured by

\Delta_{m}

Model	NAP	NTP	RTP
LSTM	0.004	0.250	0.055
CNN	0.004	0.570	0.734
Transformer	0.008	0.008	0.742

Table 3: Comparison of MTL and STL using the Wilcoxon signed-rank test. P-values are based on all task combinations and MTO methods.

Log	MTO	NAP		NTP	RTP	$\Delta_{m}$ $\downarrow$
Log	MTO	ACC $\uparrow$	macro-F1 $\uparrow$	MAE $\downarrow$	MAE $\downarrow$	$\Delta_{m}$ $\downarrow$
P2P	STL	0.73 (0.01)	0.65 (0.01)	1.07 (0.08)	4.47 (0.11)	/
	EW	0.86 (0.00)	0.78 (0.01)	1.08 (0.07)	4.73 (0.15)	-4.19
	GradDrop	0.87 (0.01)	0.78 (0.01)	1.03 (0.02)	4.41 (0.09)	-8.05
	IMTL	0.85 (0.01)	0.78 (0.01)	1.07 (0.08)	5.13 (0.52)	-1.09
Production	STL	0.44 (0.01)	0.21 (0.01)	0.99 (0.16)	4.72 (0.29)	/

Table 4: Results for LSTM. For each log, we show the average results over three random seeds (and the standard deviation) for STL, EW, and the best/worst MTO method according to

\Delta_{m}

. MAE reported in days.

Figure 3: Comparison of NAP’s train and validation loss for STL and MTL (with EW) on two datasets. The lines represent the train loss and the small shaded area captures the gap to the validation loss, respectively.

Figure 4: Average model uncertainty per dataset on the respective test set, measured by the normalized entropy. Results are based on LSTM models with EW.

Figure 5: Per-class F1-score comparison between MTL and STL models using CNN architecture for BPIC12C. The MTL model employs the NAP+NTP+RTP task combination. Activity classes are sorted by frequency.

Figure 6: Impact of task combinations on performance. Results are based on EW with the LSTM model.

Figure 7: Box plots showing

\Delta_{m}

per MTO method, aggregated over all architectures and task combinations.

Figure 8:

\Delta_{m}

performance for the large (L) and small (S) variants of the CNN model. We show the best and worst performing MTO methods as well as EW.

Finding	Recommendation
MTL mainly improves NAP, only minimal impact on temporal tasks.	Use MTL particularly when activity prediction is central.
MTL improves performance on infrequent activities.	Consider MTL when rare but relevant activities matter.
Equal weighting is often competitive.	Start with simple EW before using more complex MTO methods.
Task balancing matters under limited model capacity.	Use adaptive weighting when models are small or data is complex.
Task-wise effects can differ.	Monitor each task separately rather than relying only on aggregate scores.

Table 5: Key findings and practical recommendations.

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Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn

University of Mannheim, Mannheim, Germany · SAP Signavio, Walldorf, Germany · Technical University of Clausthal, Clausthal, Germany

Tabular Foundation Models Predictive Process Monitoring Process Mining Sequence Modeling

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Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event. While PPM research in recent years has been dominated by deep sequence models trained from scratch, such as Long Short-Term Memory (LSTM) models, foundation-model approaches---particularly large language models (LLMs)---are increasingly explored for PPM. At the same time, tabular foundation models with in-context learning capabilities offer a promising alternative but have not yet been systematically benchmarked for PPM. Thus, it remains unclear whether classical sequence-based models remain competitive in this evolving landscape. This paper compares the three modeling paradigms both conceptually and empirically through a controlled benchmark across multiple datasets and prediction tasks. The results show that sequence models consistently perform best for next activity prediction, whereas tabular foundation models are competitive on temporal tasks, with LLMs usually lagging behind despite higher cost.

Abstract

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