Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100

Andac Akyuz; Kerem Colak; Sukru Selim Calik; Zeynep Hilal Kilimci

REVIEW 5 major objections 5 minor 36 references

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that on BIST100 bank stock data, the decomposition-based linear model DLinear forecasts daily prices more accurately than LSTNet, Vanilla Transformer, and Time Series Transformer, and that SHAP and LIME make the…

desk verdict A competent BIST100 benchmark whose DLinear-wins claim is undermined by a missing validation set and missing naive baseline; fixable, but not acceptable as is. read the letter →

arxiv 2506.06345 v1 pith:TIONPCF7 submitted 2025-06-01 q-fin.ST cs.AIcs.LG

Sukru Selim Calik , Andac Akyuz , Zeynep Hilal Kilimci , Kerem Colak This is my paper

classification q-fin.STcs.AIcs.LG

keywords stockpriceforecastingtimeseriestransformersDLinearexplainableAISHAPLIMEBIST100technicalindicators

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that a set of time series models, enriched with technical indicators and explained by SHAP and LIME, can forecast daily stock prices in an emerging market, and that among the four architectures tested the decomposition-based linear model DLinear is the most accurate. On daily data from five high-volume BIST100 banks plus the XBANK and XU100 indices from January 2015 to March 2025, DLinear reports $R^2$ values between 0.984 and 0.995 and MAPE below 4% for most series, beating LSTNet, Vanilla Transformer, and Time Series Transformer on every metric. The results matter because they suggest that a simple linear decomposition can outperform attention-heavy models on this forecasting task, and that pairing such models with SHAP and LIME can make price predictions legible enough to support financial literacy among individual investors.

What carries the argument

The load-bearing object is DLinear's trend\u2013seasonality decomposition: the input series is smoothed with moving averages to separate a trend component from a residual seasonal component, each component is passed through its own linear layer, and the two outputs are summed. This is what lets a lightweight linear model capture the dominant temporal structure in bank stock prices without attention, and the study argues that it explains why DLinear beats the attention-based baselines. The interpretability machinery consists of SHAP, which assigns each feature a game-theoretic Shapley contribution averaged over the dataset, and LIME, which fits a local sparse surrogate around a single prediction; these are used to expose which technical indicators drive the forecasts.

What would settle it

Re-run the identical experiment with min-max normalization fit only on the training split, a separate validation split for hyperparameter selection, and a fixed seeded DLinear configuration; if DLinear then no longer beats LSTNet, Vanilla Transformer, and TST on most series across all five metrics, the paper's main superiority claim is falsified.

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Extended reading notes

Core claim

The central claim is that DLinear, a decomposition-based linear model, consistently outperforms the other three evaluated architectures across MSE, MAE, MAPE, RMSE, and $R^2$ on every stock and index in the dataset. The paper reports $R^2$ values from 0.984 to 0.995 for DLinear, with the strongest results on GARAN ($R^2=0.9955$, MAPE 2.34%) and the lowest percentage error on XU100 (MAPE 1.42%). It also claims that SHAP global explanations consistently rank short-lag RSI and MACD or volume features as the dominant drivers, while LIME explanations on the final day shift toward longer-horizon moving averages and volatility indicators. The study reads this global versus local divergence as evidence that the model is context-sensitive rather than contradictory.

Load-bearing premise

The load-bearing premise is that the evaluation is leak-free: min-max normalization statistics and hyperparameters such as sequence length are chosen without consulting the test period, so the reported test-set $R^2$ values and DLinear's margin are not inflated by information from the future.

Editorial extensions

If this is right

DLinear gives a cheap, high-accuracy baseline for BIST100 bank forecasting, reaching near-99% $R^2$ at a fraction of the compute of attention models.
On this dataset, complex attention architectures buy little accuracy, so practitioners can prefer the simpler model and reserve transformers for settings with longer or more nonlinear dependencies.
SHAP's global emphasis on short-lag RSI and MACD suggests that momentum and volume indicators carry most forecasting signal for Turkish bank prices over this decade.
LIME's local emphasis on moving averages, Bollinger Bands, and Ichimoku components means explanations can change sharply by date, so forecasts should be accompanied by both global and local attribution.
The same 80/20 split with technical-indicator enrichment can be replicated for other BIST sectors or emerging markets, giving a direct benchmark for XAI-augmented forecasting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

We infer that a testable extension beyond the paper is to apply the same four-model comparison to other emerging-market bank panels; the expectation from these results is that DLinear's linear decomposition retains its edge whenever prices are dominated by strong trends, and loses it in regimes with regime shifts or non-stationarity.
The reported gap between SHAP and LIME suggests a practical design principle for investor-facing tools: show a global driver ranking and a local \u2018why this prediction\u2019 explanation together, because either alone gives an incomplete picture.
Because the dataset ends in March 2025 and includes high-inflation episodes, a natural stress test is to retrain on data containing a sudden crisis or policy shock and check whether DLinear's decomposition absorbs the break or whether its $R^2$ drops below the transformer models.
The paper's reliance on technical indicators only leaves room for adding macro and sentiment inputs; if those features carry independent signal, the RSI-dominant SHAP rankings would shift, changing the financial-literacy story from momentum-following to fundamental context.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, this is a routine application of known models to a new dataset, not a methodological advance. Second, the main result—DLinear beats four transformer variants—probably comes from a test-set selection artifact. Not because the authors intended anything shady, but because Section 3.2.4 never mentions a validation set, and it says hyperparameters and sequence length were selected using MSE, RMSE, and MAPE. On the natural reading, the same 20% test window was used to pick configurations and then to report metrics. That would make DLinear's superiority a selection effect.

What it does well: it is a clean, readable case study on ten years of daily BIST100 bank data and two indices. Seven assets, four architectures, standard metrics, and SHAP/LIME applied consistently. The authors also state their hyperparameter choices per model, which is more than many papers do. The qualitative finding—that a simple linear decomposition model outperforms transformer variants—matches the earlier literature (e.g., Zeng et al. 2023), so the direction is plausible, even if this paper doesn't prove it.

The soft spots. The validation protocol is the load-bearing one, as above. There is also no naive baseline. R² of 0.98–0.99 on trending price levels is exactly what a last-value forecast would produce, so the absolute numbers are uncontextualised. Normalization is described as applied 'prior to training,' with no statement that min-max statistics are training-only; if they were fit on the full series, that's future leakage. And the training set is shuffled for a time series task, which breaks temporal ordering between samples. I'd call that a design mistake, though it may not change the relative ranking much.

There are also direct contradictions between the text and the tables. The LSTNet paragraph says AKBNK has R² 0.993 and GARAN 0.995, but Table 4 shows 0.9800 and 0.9836. The same paragraph attributes to LSTNet the MSE 0.104 and RMSE 0.323 for ISCTR, which are DLinear's numbers. These are easy to fix but currently undermine trust in the results section.

Who should read it: anyone who wants a quick benchmark of simple versus transformer models on Turkish bank data, or an example of SHAP and LIME applied to DLinear. It is not a contribution to forecasting methodology.

My recommendation: this deserves peer review but not acceptance as is. A serious referee should require a naive baseline, a proper train/validation/test protocol, a statement on normalization statistics, and corrected tables. After that, it might be a publishable case study. So I would not desk-reject it, but I would not let it through unchanged.

Referee Report

5 major / 5 minor

Summary. This manuscript proposes an empirical framework that combines four time-series forecasting models—DLinear, LSTNet (also written LTSNet), Vanilla Transformer, and Time Series Transformer—with a set of technical indicators to predict daily closing prices of five Turkish banks and two BIST indices. The authors report performance metrics (MSE, MAE, MAPE, RMSE, R²) for each model and use SHAP and LIME to interpret the DLinear model's predictions. The central claim, stated in the Highlights and Section 5, is that DLinear consistently outperforms the other architectures across all metrics.

Significance. If the evaluation were sound, the paper would offer a useful case study of simple linear decomposition models versus transformer architectures in an emerging market, and a concrete demonstration of how XAI can be attached to a time-series forecaster. The authors are transparent about the limitations of their dataset (Section 6.1). However, the significance is undercut by three unresolved methodological questions: whether the test set was used for model selection, whether normalization leaks test information, and whether any naive baseline would achieve similar R² values. These concerns directly affect the paper's central quantitative claim.

major comments (5)

[§3.2.4] No validation set is described. The paper defines only an 80/20 train–test split and says that sequence length and hyperparameters were "selected based on performance metrics such as MSE, RMSE, and MAPE" and that DLinear's "best results" were determined by configurations that "minimize error metrics." On the most natural reading, the same 20% test window was used both to choose configurations and to produce the reported test metrics. This makes the DLinear advantage in Section 5 a potential selection artifact rather than an out-of-sample result. The authors must introduce a proper validation split (or nested cross-validation) and report metrics from a single final evaluation on untouched data.
[§3.2.4] The text states that "all variables are normalized to the [0, 1] range using min-max normalization prior to training" but does not state that the minimum and maximum values are computed exclusively on the training split. If the scaling parameters are computed on the full dataset, information from the test period propagates into the training inputs, and the reported R² values in Tables 3–6 are inflated. The authors must state the exact procedure and, if necessary, re-run the experiments with training-only scaling.
[§4, Tables 3–6] The comparison contains no naive or statistical baseline, such as a persistence forecast or AR(1). Because the targets are trending price levels, a model that simply repeats the last observed value would also produce R² values in the 0.98–0.99 range. Without such a baseline, the claim in the Highlights and Section 5 that DLinear shows "superior forecasting capability" is not established; the authors should add at least a persistence and an ARIMA/AR baseline to the benchmark tables.
[§3.2.4] Shuffling the training set once "before the training process begins" is incompatible with a causal time-series forecasting setup. If the model's input windows are constructed after shuffling, each window can contain observations that temporally follow the target, which is a form of lookahead leakage; if the windows are constructed before shuffling, the description is misleading. The authors need to clarify the exact order of windowing and shuffling and justify why shuffling is used for a temporal task.
[§4, Tables 3–6] The reported numbers contain internal inconsistencies that undermine confidence in the results. For example, Table 3 lists GARAN's R² as 0.9955 and QNBTR's as 0.9847, but the text refers to "the lowest R² of 0.984%" for QNBTR; the paragraph on Table 4 claims GARAN has "the highest R² of 0.995", although Table 4 reports 0.9836 for GARAN; and the same paragraph attributes an MSE of 0.104 and RMSE of 0.3234 to ISCTR, which are actually the DLinear values from Table 3, not the LSTNet values. The model is also called LTSNet in Table 2 and LSTNet elsewhere. All of these need to be corrected and the tables re-verified.

minor comments (5)

[Abstract and Table 2] The model name is written as "LTSNet" in the abstract and Table 2 but as "LSTNet" in Section 3.2.1 and Table 4; standardize the name throughout the manuscript.
[Figures 4–10] The study claims to evaluate "transformer models," but the SHAP and LIME analyses are applied only to DLinear; clarify that the XAI part concerns only the DLinear model.
[§4] Figure 3 is discussed before Figure 2 in the text; renumber either the figures or the references.
[§3.2.4] The paper does not state the prediction horizon (single-step versus multi-step); specify whether the models output one day ahead or multiple days ahead.
[Figure 6] Figure 6 mentions "MA_200" and "MA_300" as influential features, but Table 1 does not include these indicators; either add them to Table 1 or correct the figure caption.

Circularity Check

1 steps flagged · score 6.0 of 10

DLinear's reported superiority is a selection artifact: sequence length and hyperparameters are chosen on the same test metrics later reported as the model's out-of-sample performance.

fitted input called prediction [Section 3.2.4 (Methodology) and Section 5 (Discussion); Tables 3-6]
"The dataset is divided into training and testing subsets based on predefined split ratios. For each dataset used in this study, a fixed train-test split ratio of 80%–20% is applied ... In this study, multiple values of sequence length are systematically tested for each model, and the most suitable value is selected based on performance metrics such as MSE, RMSE, and MAPE."

The methodology defines only an 80/20 train/test split and never introduces a validation set. Sequence length and, for DLinear, epochs, batch size, learning rate, and dropout are then chosen by minimizing error metrics such as MSE, RMSE, and MAPE. In the absence of any other evaluation partition, those tuning metrics are the same test-set metrics reported in Tables 3-6 and used in Section 5 to conclude that 'DLinear consistently outperforms its counterparts across all performance metrics.' The headline result is therefore not an out-of-sample prediction but the output of selecting the configuration and model that minimize the very figures later presented as evidence; the reported advantage is forced by the selection criterion.

full rationale

This is an empirical paper with no mathematical derivation chain, so there is no equation-level circularity and no load-bearing self-citation (the reference list contains no works by the present authors). The one substantive circularity concern is the evaluation protocol: the paper describes only a train/test split and then tunes sequence length and hyperparameters using MSE/RMSE/MAPE, with DLinear's final setup chosen as the one that minimizes error metrics. Since no validation partition is described, the natural reading is that the test set was used to select configurations, making the reported DLinear superiority a selection artifact rather than an independent forecast. The absence of a naive or random-walk baseline is a completeness and interpretability problem but not itself a circularity; it is noted only to explain why the absolute R² values are uncontextualised. On the stated protocol, the central comparative claim is partially circular, warranting a score of 6.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new entities are introduced. The results rest on a set of domain assumptions about feature usefulness, normalization, shuffling, and the representativeness of a single split, plus the standard regression metrics. The hyperparameters are fitted to the test data, which is the most consequential choice.

free parameters (4)

DLinear hyperparameters = epochs=100, lr=1e-3, batch_size=32, seq_len=10, dropout=0.0
Selected by systematic search using test-set metrics (Section 3.2.4).
Vanilla Transformer hyperparameters = epochs=50, lr=1e-4, batch_size=64, seq_len=10, dropout=0.1
Selected by systematic search using test-set metrics (Section 3.2.4).
TST hyperparameters = epochs=50, lr=1e-4, batch_size=32, seq_len=5, dropout=0.1
Selected by systematic search using test-set metrics (Section 3.2.4).
LTSNet hyperparameters = epochs=100, lr=1e-5, batch_size=64, seq_len=5, dropout=0.2
Selected by systematic search using test-set metrics (Section 3.2.4).

assumptions (5)

domain assumption Technical indicators (EMA, RSI, ATR, Bollinger, Ichimoku) computed from OHLCV data contain information useful for predicting future closing prices.
The entire feature engineering in Section 3.2.3 rests on this.
domain assumption Min-max normalization is applied without lookahead leakage.
Section 3.2.4 states normalization is applied prior to training, but does not specify that statistics are computed on the training split only.
domain assumption Shuffling the training set preserves the validity of the time series forecasting setup.
Section 3.2.4: 'the training set is shuffled once before the training process begins.' Standard time series practice usually preserves temporal order.
domain assumption The 80/20 chronological split gives a representative test period.
Section 3.2.4: 'a fixed train-test split ratio of 80%-20% is applied.' No consideration of regime shifts or multiple splits.
standard math R², MAPE, RMSE, and MAE are appropriate metrics for comparing forecast accuracy across models.
Used in Section 4 without justification.

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Cite this review

Pith. "Pith review of Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100." pith.science (2026). https://pith.science/paper/TIONPCF7

@misc{pith2026250606345,
  author       = {Pith},
  title        = {Pith review of: Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TIONPCF7}},
  note         = {Machine review of arXiv:2506.06345}
}

read the original abstract

Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets.

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Figures reproduced from arXiv: 2506.06345 by the authors.

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