Understanding the Limits of Deep Tabular Methods with Temporal Shift

• Temporal shifts create discrepancies between the mapping learned before Ttrain and the mapping required after Ttrain, making deployment predictions ineffective.Source: §1 · pp. 1–2
• Randomly splitting training and validation data can outperform temporal splitting, although it is notably unstable under temporal shifts.This finding motivates a closer examination of training lag and validation bias.Source: §1 · p. 2
• The proposed training protocol leverages temporal data to achieve performance comparable to random splitting while providing better stability.Source: §1 · p. 3
• Deep-layer representations gradually lose the trend and periodic information present in raw temporal data, restricting temporal prediction ability.Source: §1 · p. 3
• A lightweight Fourier-based temporal embedding adds learnable periodic and trend information while preserving original model capabilities and enabling adaptation after deployment.Source: §1 · p. 3

2.1. Tabular Machine Learning

• Tabular methods include tree-based, token-based, retrieval-based, ensemble-based, and MLP architecture approaches.Source: §2.1 · p. 3

2.2. Distribution Shift in Tabular Data

• Distribution-shift research addresses either partially available target data through transfer or adaptation, or unavailable target data through robustness and shift-pattern learning.Source: §2.2 · p. 3
• Temporal splits preserve sequential dependencies in forecasting but generally underperform random splits in tabular contexts, motivating systematic analysis of splitting strategies.Source: Figure 3 · p. 4
• TabReD introduces temporal shifts in tabular data and advocates temporal splits, especially for industrial data with visible timestamps.Source: §2.2 · p. 4
• Distribution-shift work also includes domain generalization, domain adaptation, test-time adaptation, and continual adaptation to sequential domain changes.Source: §2.3 · p. 4

Tabular data

• A tabular dataset consists of feature-label pairs, with the objective of learning a mapping that predicts labels for unseen instances.Features may be continuous vectors, while labels may represent classes or real values.Source: §3 · p. 4

Tabular models

• The prediction function can combine feature extraction with a prediction head, including neural networks, ensembles, or retrieval-based predictors.Ensembles average multiple model outputs, while retrieval methods use learned representations and nearest-neighbor prediction.Source: §3 · p. 4

Temporal setting

• Temporal tabular data partitions observations by timestamp into pre-Ttrain training and validation data and post-Ttrain test data.The pre-Ttrain data are further split into training and validation sets, while deployment evaluates on later observations.Source: §3 · p. 4

Temporal distribution shift

• Temporal distribution shift occurs when sequentially collected data evolve over time, changing feature, label, conditional, or even feature and label spaces.The formal changes include p(x_t) ≠ p(x_t′), p(y_t) ≠ p(y_t′), and p(y_t | x_t) ≠ p(y_t′ | x_t′).Source: §3 · pp. 4–5

Motivation

• Conventional tabular methods often assume stationary distributions through shared feature representations, leaving temporal dynamics insufficiently examined.The section motivates investigating how temporal changes affect the effectiveness of these methods.Source: §3 · p. 5

4.1. Training Lag

• 1.62% average improvement across three methods supports minimizing training lag by using instances closer to test time for training.ModernNCA improves most, by 2.19%, indicating that no-lag candidates can matter across architectures.Source: §4.1 · p. 5

4.2. Validation Bias

• 0.59% average improvement from reducing validation bias is larger for TabM, which improves by 0.83%.The comparison indicates that ensemble methods are especially sensitive to validation bias.Source: §4.2 · pp. 5–6

4.4. Our Temporal Split

• The protocol minimizes training–test lag, reduces train–validation versus train–test interval differences, and can use opposite-direction validation for most tasks.These principles are intended to preserve validation equivalence while improving generalization to the test period.Source: §4.3 · p. 6, §4.4 · p. 7

4.3. Bridging the Past and Future in Temporal Split

• A temporal split aligned with the test shift and zero training lag improves performance, but its effectiveness depends on dataset sampling regularity.Nonuniform temporal sampling can make equal-sized validation sets inconsistent in their time intervals.Source: §4.3 · pp. 6–7, §4.4 · p. 7

4.4. Our Temporal Split

• 2.18% vs. 2.17% average improvement shows the proposed temporal split matches random splitting, while standard-deviation increase is 16.69% versus 153.81%.The comparison is relative to the baseline temporal split, with the proposed temporal split offering substantially better stability.Source: Table 1 · p. 6, §4.4 · p. 7

5.1. Temporal Embedding

• Temporal embedding restores representation patterns closer to the original distribution, including diagonal stripes corresponding to distinct temporal phases.The recovered structure supports its adaptive role in preserving temporal information during training.Source: §5 · p. 7

5. What is Lost in Temporal Training?

• MLP representations lose trends, long-term periodicity, and fine short-term cycles, retaining mainly weekday-versus-weekend distinctions.This loss of temporal information is associated with weaker performance on datasets containing clear weekly or yearly cycles.Source: §5 · pp. 7–8

5.2. Adaptive Temporal Shift Mitigation

• Temporal embedding enables phase-specific knowledge and adjusts the mapping f_t, providing a lightweight adaptive approach to temporal generalization.The method embeds temporal information rather than requiring a separate adaptation procedure.Source: Figure 6 · p. 8

5.1. Temporal Embedding

• The proposed Fourier-based temporal embedding consistently outperforms non-learnable embeddings and the learnable PLR variant on the TabReD benchmark.Non-learnable embeddings yield limited improvement, while PLR helps MLP only slightly.Source: Table 2 · p. 8

Temporal Embedding Design

• The temporal embedding combines Fourier-based periodic components with an optional trend term to encode timestamp dependencies.The periodic component uses predefined yearly, monthly, weekly, and daily priors; the trend term adds a standardized timestamp for linear temporal shifts.Source: §6 · pp. 8–9
• Fourier expansions with learnable projection and ReLU produce sparse representations that emphasize salient temporal frequencies.Multiple periodic components are projected through a learnable linear layer, while ReLU promotes sparsity in the learned periodic representation.Source: §6 · p. 8

Experimental Results

• Directly using timestamps as numerical or decomposed features can degrade performance, while PLR embedding is unstable and does not consistently improve MLP-PLR.The authors attribute PLR’s limitations to discarded linear trends and difficulty capturing periodic patterns accurately.Source: §6 · p. 9
• Most methods improve after adopting the temporal embedding, while MLP-PLR and TabM gain less because existing PLR-based representations are incompatible with them.The comparison is reported in Figure 7, and the authors note that these models improve substantially more with the proposed embedding than with existing numerical embedding approaches.Source: §6 · p. 9, Figure 7 · p. 9
• ModernNCA and TabR achieve strong performance after combining the appropriate training protocol with temporal embedding, recovering practical utility for retrieval-based methods.Retrieval-based methods had previously been especially affected by distributional shift.Source: §6 · p. 9

Representation Analysis

• The proposed embedding restores diagonal temporal patterns in model representations, indicating that periodic information lost by MLP is recovered.The post-embedding MMD heatmap more closely resembles the original data and represents separate temporal phases within the period.Source: §6 · p. 9

Conclusion

• Combining the new temporal split with temporal embedding produces marked performance improvements, particularly for retrieval-based models under temporal shifts.The paper presents the training protocol and embedding as complementary strategies for improving model adaptability.Source: §7 · p. 9
• The analysis finds that periodic and trend information is often lost in learned deep tabular representations.The temporal embedding is introduced to compensate for this loss by incorporating timestamp information.Source: §7 · p. 9
• The study highlights training lag, validation bias, and validation-set equivalence as important factors in temporal tabular learning.These factors motivate the proposed splitting strategy and the broader training-protocol analysis.Source: §7 · p. 9

Impact Statement

• The proposed training protocol and temporal embedding offer practical improvements for deploying existing tabular models in open environments.The impact statement frames these methods as responses to temporal distribution shifts in real-world applications.Source: Impact Statement · p. 10

Dataset and Evaluation Setup

• The study adopts the full TabReD dataset without modification and summarizes its datasets in Table 3.Task descriptions are attributed to Rubachev et al. (2025).Source: A. Dataset Explanation · p. 13, Table 3 · p. 13

Evaluation Metrics

• Reported improvements are percentage AUC increases for classification and percentage RMSE decreases for regression, with Figure 2 and Figure 7 comparing methods relative to MLP.Figures 3, 2, and 7 and Tables 1 and 2 use these improvement conventions.Source: A. Dataset Explanation · p. 13

Evaluation Setup

• The reproduced comparison removes numerical encoding and fixes categorical encoding to one-hot where necessary to align the evaluation across methods.This adjustment is intended to ensure a fair comparison with TabReD’s preprocessing choices.Source: A. Dataset Explanation · p. 13

Dataset Caveat

• On HD, severe class imbalance reduces naive MLP, SNN, and DCNv2 AUC to approximately 0.55, whereas other methods typically exceed 0.80.Because this discrepancy makes direct comparison uninformative, the paper reports percentage improvement over MLP using a robust average.Source: A. Dataset Explanation · p. 13

Sampling and Validation Caveat

• Validation-set equivalence conclusions are less reliable for highly non-uniform temporal sampling and stronger for datasets with uniform sampling over time.The analysis equalizes instance counts across splits, and the temporal sampling distributions are shown in Figure 8.Source: Figure 8 · p. 13, A. Dataset Explanation · p. 14

Experimental Setup

• Hyperparameters follow Rubachev et al. (2025), with 100 Optuna trials for most methods and 25 trials for FT-T and TabR.The temporal-embedding search separately tunes periodic order and linear trend to reduce computational overhead.Source: B. Experimental Setup · p. 14
• The temporal-embedding hyperparameter space includes powers of two represented by PowerInt[a, b], exemplified by PowerInt[1, 5] = {2, 4, 8, 16, 32}.The search space is documented in Table 4 and includes integer powers of two.Source: Table 4 · p. 14, B. Experimental Setup · p. 14
• Random-split results average 45 runs across three splits and 15 seeds per split, with standard deviation computed over all runs.This captures variability from both split selection and training seeds.Source: B. Experimental Setup · p. 14

C.1. Splitting Strategies

• The proposed temporal split consistently improves performance for Mambular and TabPFN-v2 relative to their original splitting procedures.These additional model families test whether the refined protocol generalizes beyond the main benchmark methods.Source: §C.1 · p. 15, Table 5 · p. 15
• The splitting-strategy comparisons report both performance and standard deviation across the original temporal, random, and proposed temporal splits.Tables 7 and 8 provide the corresponding performance and variability results for the TabReD benchmark.Source: §C.1 · p. 15, Table 7 · p. 16, Table 8 · p. 17
• 2.18% versus 2.17% without penalty, and 2.43% versus 2.21% under strict penalty, show comparable performance but stronger robustness for the proposed temporal split.The comparisons use robustness scores with k = 0 and k = 2 penalties; increasing the penalty makes the proposed split’s advantage more pronounced.Source: §C.1 · p. 18, Figure 9 · p. 18

C.2. Temporal Embeddings

• Fixed prior cycles improve ModernNCA by 0.30%, whereas adjustable cycles reduce performance by -2.48%.The authors argue that fixed cycles are more stable and interpretable when validation data are not fully accurate or complete cycles are unavailable.Source: §C.2 · p. 19, Table 10 · p. 19
• All evaluated methods improve after temporal embedding, with an average performance improvement of 0.74%.Table 12 reports AUC for classification and RMSE for regression before and after applying the embedding.Source: §C.2 · p. 19, Table 12 · p. 20

C.3. Performance Rankings

• TabM, CatBoost, and XGBoost consistently perform well, while ModernNCA excels under random and proposed splits and MLP-PLR shows the opposite pattern.These comparisons are based on average performance rankings across splitting strategies.Source: §C.3 · p. 20, Table 13 · p. 20
• TabM gains 0.07% performance and rises 0.875 average ranking positions after temporal embedding, despite a -0.12% reduction in its relative improvement over MLP.No other evaluated method experiences a performance drop after adding the embedding.Source: §C.3 · p. 20

C.4. Model Representations

• Before temporal embedding, TabM representations show grid structure without diagonal stripes, indicating mainly weekday-versus-weekend temporal modes.After embedding, grid and diagonal patterns both appear, indicating finer-grained temporal knowledge.Source: §C.4 · p. 21, Figure 10 · p. 21