{"id":"9b90b945-eb46-43e3-8cee-0e91fc0f4648","arxiv_id":"2507.20641","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A fuzzy sliding-window plus partially asymmetric convolutional model reports state-of-the-art MAE/RMSE on most of 43 benchmark time series datasets.","lead":"This paper proposes a convolutional time series forecaster that fuzzifies each sliding window before extracting features with variable-length asymmetric filters. It reports lower errors than 18 baselines on most of 43 public datasets, but provides no code or experimental protocol.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The SOTA claim rests on a feature tensor whose construction is internally inconsistent: Eq. (6) places the right-hand segment outside the universe of discourse, and the PCP cannot losslessly align all elements, so the experiments are not reproducible as specified.","rationale":"The paper's central claim is an empirical SOTA claim. For such a claim to be even assessable, the method that produced the tables must be specified unambiguously. The most load-bearing point is therefore the well-definedness of the feature tensor, since every later stage consumes it. The reader's weakest_assumption identifies exactly this PCP alignment issue; I agree. If the PCP is inconsistent, the experiments cannot be a faithful execution of the described method, and the SOTA claim is unsupported regardless of how plausible the numbers look. Secondary problems (no code/data, missing splits and horizons, the S10 0.07/0.07 entry) would remain even if the PCP were repaired, but they are not as fundamental: a precise reimplementation could in principle resolve them. The concrete test above settles the primary concern directly and cheaply. Since the reader already reached REJECT and this analysis reinforces that verdict, no verdict change is needed.","tokens_in":23921,"tokens_out":6134,"duration_ms":68526,"concrete_test":"Implement Algorithm 1 and Eqs. (6)–(9) exactly as written on a fixed synthetic series (e.g., n=32 differences, S=8, N=3 intervals). For each element ς_i in a window, compute the padded/cropped vector E''_Crop(ς_i) and check two conditions: (1) every element of every vector lies in [α_l,α_r]; (2) the index of ς_i+ϱ within the vector is the same for all i. Condition (1) already fails for any φ≥1 because of the α_r+(φ+1)τ term; if one corrects the sign and reruns, condition (2) fails unless a concrete SL rule is supplied. A 20-line Python script settles the question; no full training is needed.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing condition for the claimed SOTA results is that the fuzzified feature tensor Y_{e,i} produced by §4.3 (Eqs. 6–9 and Algorithm 1 lines 6–10) is well-defined and identical for every sliding window. It is not. In Eq. (6), the expanded vector for element ς_{i+S−1} is [α_l, ..., α_l+φτ, ς, α_r+(φ+1)τ, ..., α_r]^T with φ∈[1,N], τ=(α_r−α_l)/N. For every φ≥1, the right-hand segment starts at α_r+(φ+1)τ > α_r, outside the universe of discourse U=[α_l,α_r]; the expression '..., α_r' cannot be a segment within U. Even if this is read as a typo, the padding-crop policy (PCP) in Eqs. (8)–(9) is not a well-defined algorithm: the natural lengths of the two sides depend on φ, so after padding the shorter side and cropping to the shortest length SL, the position of the reconstructed element ς_i+ϱ is not invariant across elements. Algorithm 1 never states how SL is computed or how cropping preserves φ and ϱ. Consequently the input to the bilateral atrous and convolution stages is ill-defined; the numerical results in Tables 2–5 cannot be reproduced from the paper's own specification. This is an internal inconsistency, not merely missing code.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a fuzzy-time-series-based convolutional forecasting model combining an improved fuzzification scheme, a bilateral atrous algorithm, and a partially asymmetric convolution architecture. The authors claim state-of-the-art performance on most of 43 time series datasets compared with 18 baselines, supported by MAE/RMSE tables, a Nemenyi test figure, and a parameter study. The central derivation in Section 4.3 is the padding-crop policy (PCP) that is supposed to align fuzzified element vectors to a common length while preserving interval-position and tendency information; this construction feeds the subsequent bilateral atrous and convolution stages.","tokens_in":24258,"tokens_out":3820,"duration_ms":45044,"significance":"If the proposed construction were sound and the experimental protocol were fully specified, the paper would offer a reasonably novel combination of fuzzy preprocessing with asymmetric convolutions and a broad empirical comparison, and the claimed improvements on high-error datasets such as KDD, A.E.D, and F-M would be practically interesting. The paper also has some credit-worthy ingredients: the universe of discourse is set automatically from the data standard deviation, the bilateral atrous idea is explicit, and the comparison includes a large set of modern baselines. However, the significance of the central SOTA claim is undermined by a load-bearing internal inconsistency in the feature-construction equations and by an experimental presentation that lacks the protocol details needed to reproduce or statistically support the reported results.","major_comments":[{"comment":"Equation (6) is internally inconsistent. For E(ς_{i+S−1}) = [α_l, ..., α_l + φτ, ς, α_r + (φ+1)τ, ..., α_r]^T with τ = (α_r − α_l)/N and φ ∈ [1,N], the right-hand segment begins at α_r + (φ+1)τ, which is strictly greater than α_r for every φ ≥ 1, so that segment lies outside the universe of discourse U = [α_l, α_r]. The expression \"..., α_r\" cannot denote a sub-vector of U, and the same issue appears in Eq. (8). Because the fuzzified feature tensor Y_{e,i} is defined from these expanded vectors, the model input to the bilateral atrous and convolution stages is not well-defined as written.","section":"§4.3, Eq. (6)"},{"comment":"The padding-crop policy (PCP) is not a well-defined algorithm. The natural lengths of the two sides of E′(ς_{i+S−1}) depend on φ: the left side has about φ entries and the right side about N − φ entries (if the vector were inside U). After padding the shorter side and cropping to the \"total shortest length SL\", the retained interval position α_l + φτ and the tendency-accumulation value ϱ are not invariant across elements, since cropping removes different amounts from different sides for different φ. Algorithm 1 never states how SL is computed, how cropping preserves φ and ϱ, or how the output Y_{e,i} is indexed. Consequently the feature tensor entering Eq. (10) is ill-defined, and the numerical results in Tables 2–5 cannot be reproduced from the paper's own specification.","section":"§4.3, Eqs. (8)–(9), Algorithm 1"},{"comment":"No experimental protocol is reported. The text gives the optimizer, scheduler, loss, and epoch count, but does not specify train/validation/test splits, forecast horizons per dataset, number of repeated runs, seeds, or error bars. The tables appear to contain single-run MAE/RMSE values, which is insufficient to support the abstract's claim that the SOTA results are \"fully verified.\" A statistical comparison would require at least multiple seeds with reported variability and a clear statement of how hyperparameters S, V, H, η, K were selected for each dataset.","section":"§5, Tables 2–5 and implementation details"},{"comment":"The S10 row reports MAE = 0.07 and RMSE = 0.07 for the proposed method. Since RMSE ≥ MAE always, equality can occur only if every absolute error is identical; for a continuous solar irradiance series this is highly implausible and suggests a data-handling or reporting error. Additionally, Table 1 labels M4 Weekly and M4 Hourly with the abbreviation \"M1 Y\" (the same as M1 Yearly), and the table captions call the datasets \"M4 W\" and \"M4 H\" in Tables 2–3; these wrong labels undermine the reliability of the benchmark tabulation.","section":"Tables 4 and 5, S10 row; Table 1"},{"comment":"The paper claims that the fuzzification process \"does not require human involvement\" and automatically assigns global information, but the method still relies on manually configured hyperparameters: sliding-window size S, vertical/horizontal filter lengths V and H, channel growth rate η, and repetition count K. Section 5.3 shows that performance varies substantially with S and η, yet no per-dataset selection rule or search protocol is given. The reproducibility advantage claimed in the Introduction is therefore overstated.","section":"§4.2, §4.5, §5.3"}],"minor_comments":[{"comment":"The survey paragraph beginning \"Fine-grained advances in FTS (2022–2025)..\" contains inconsistent citation formatting (e.g., \"PhamToan, Dinh and VoThiHang\" without initials), duplicated reference entries ([16] and [17] are identical), and a long chain of references on evidential reasoning and medical image segmentation ([34]–[56]) that is only tenuously connected to fuzzy time series forecasting.","section":"§2"},{"comment":"The symbol φ is used both as the length of each interval in the text and as the interval index in Eq. (6), while τ is introduced as the interval length; this notational clash obscures the intended construction and should be resolved.","section":"Eq. (6) and surrounding text"},{"comment":"The spelling \"BlinearLayer\" in Figure 2 differs from \"BLINEAR\" in Eq. (16); please unify the notation.","section":"Figure 2 and Eq. (16)"},{"comment":"The last comment in Algorithm 1 (\"avoid information leak\") describes an important design detail but is never explained in the body text; its mechanism and effect on the difference-series restoration should be described.","section":"Algorithm 1"},{"comment":"The caption of Figure 4 calls the test a \"Friedman Test\" while the text describes a Nemenyi post-hoc test; no critical difference (CD) values or significance levels are reported in the figure, so the visual claim of \"absolute leading performance\" is not quantitatively supported.","section":"Figure 4 and §5.2"},{"comment":"The operator ◦ in Eq. (11) is not defined; the atrous operation was defined with summation in Eq. (2), and the relationship between the two notations should be clarified.","section":"Eq. (11)"}],"recommendation":"reject","confidential_remarks":"The paper's central construction in §4.3 is internally inconsistent, and the experimental section lacks the protocol detail needed to verify the SOTA claim. The extensive self-citation cluster in Section 2 (references [34]–[56]) is largely peripheral to the proposed method and inflates the bibliography; the authors should prune it and cite only directly relevant prior work. Given the current state, the manuscript is not publishable in its present form, and a rejection is appropriate."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper is a plausible-sounding neural forecasting method that does not survive contact with its own equations. The core fuzzified feature construction in Section 4.3 is internally inconsistent, and the reported \"state-of-the-art\" numbers cannot be reproduced from the manuscript. I agree with the stress-test note: Eq. (6) places the right-hand segment of the expanded vector beyond the universe of discourse for every φ≥1, and the padding-crop policy does not specify how alignment is achieved. This is not a minor typo—the input to the entire convolutional stack depends on this tensor being well-defined.\n\nWhat is genuinely new? The variable horizontal/vertical filter lengths in the \"partially asymmetric\" convolution is a small but real variation on ACNet-style decomposition, and the idea of using fuzzy intervals to assign global position and tendency to each sliding-window element is interesting in principle. The paper also deserves credit for evaluating on 43 benchmark datasets and for including a Friedman/Nemenyi comparison, which many short forecasting papers omit.\n\nThe soft spots are serious. The SOTA claim rests on single-run tables with no train/test split, no forecast horizons, no per-dataset hyperparameters, and no code. The S10 row reports MAE = RMSE = 0.07, which is mathematically implausible unless every absolute error is identical. Table 1 mislabels M4 Weekly and M4 Hourly as \"M1 Y\". The PCP algorithm is under-specified: it never says how SL is computed or how cropping preserves the interval position and tendency offset. And the \"bilateral atrous\" description uses undefined notation (e.g., P, δl, δr). None of this is fatal to the underlying architecture idea, but as written the experiments are not checkable.\n\nI would not send this to a serious referee in its current form. The central definitional flaw and the absence of reproducibility details make it a desk reject. If the author fixes Eq. (6)–(9), clarifies the PCP, adds error bars, and releases code, the partially-asymmetric convolution idea could become a modest but citable contribution for the fuzzy-time-series-CNN niche. Right now, it is a sketch with an unverifiable headline.\n\nRecommendation: reject, encourage resubmission after major revision, and ask the author to provide an implementable algorithm specification.","headline":"A promising architecture sketch undermined by an ill-defined core transformation and unverifiable SOTA claims.","tokens_in":24741,"tokens_out":2934,"would_cite":false,"duration_ms":31443,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that fuzzifying each sliding-window element with global position and tendency information, then processing it with bilateral atrous and partially asymmetric convolutions, yields state-of-the-art time series forecasts on…","keywords":["adaptive fuzzy time series","partially asymmetric convolution","sliding window","bilateral atrous algorithm","global information","multi-scale feature fusion","time series forecasting"],"falsifier":"Inspect Eq. (6) with $\\varphi = N$: the vector's right side starts at $\\alpha_r + (N+1)\\tau$, beyond the universe of discourse boundary $\\alpha_r$, so the padding and cropping described in Eq. (9) cannot be lossless for all elements simultaneously; computing the padded-cropped vectors for boundary elements would settle whether the fuzzified features are well-defined. A complementary statistical check is to replace the PCP with fixed one-sided padding and see whether the reported MAE/RMSE on the 43 datasets changes materially.","tokens_in":23668,"feed_emoji":"📈","tokens_out":6115,"duration_ms":59195,"temperature":0.7,"pith_summary":"This paper tries to establish that time-series forecasting can be made substantially more accurate by enriching each element of a sliding window with global positional and tendency information drawn from the whole series, and then processing that enriched representation with a tailored convolutional stack. The proposed pipeline fuzzifies the differenced series, embeds each element in a fuzzy value range with an assigned interval position and a tendency accumulation, thins the reconstruction with a bilateral atrous algorithm, and applies partially asymmetric convolutions that use different filter lengths horizontally and vertically to mine sub-windows within windows. The payoff claimed is state-of-the-art results: the lowest mean absolute error and root-mean-square error on most of 43 benchmark datasets compared with 18 baselines. If true, the work would show that careful global-context injection plus a lightweight convolutional design can rival or beat transformer-based forecasters.","feed_headline":"Fuzzy-CNN hybrid forecasts with lowest error on most of 43 datasets","feed_subtitle":"The model wraps each time point in global context, then mines fine-grained sub-windows with variable filter lengths.","key_machinery":"The load-bearing mechanism is the fuzzified feature transformation: each difference-series element $\\varsigma_i$ in a sliding window is embedded into a vector spanning the universe of discourse $\\mathcal{U} = [\\varsigma_{\\min} - \\sigma, \\varsigma_{\\max} + \\sigma]$, with the element placed at its assigned interval position $\\varphi$ and augmented by a tendency accumulation $\\varrho$, then a padding-crop policy (PCP) is meant to align all reconstructed vectors to a common shortest length $SL$. This aligned tensor is thinned by a bilateral atrous algorithm (BAA) that convolves only the two sides of each reconstructed element, and then processed by partially asymmetric convolutions, i.e., separate vertical $f_V$ and horizontal $f_H$ filters with possibly different lengths, which build sub-windows inside the original sliding window and are fused with a residual-like branch of average pooling and $1 \\times 1$ convolution.","core_discovery":"The paper's central claim is that time-series forecasting can be improved by giving every element inside a sliding window a globally assigned position in a fuzzy universe of discourse plus a tendency accumulation value, and then learning from the resulting reconstructed vectors with a bilateral atrous algorithm followed by partially asymmetric convolutions whose horizontal and vertical filter lengths can differ. On 43 benchmark datasets spanning yearly to hourly frequencies across domains such as energy, traffic, weather, finance, and web traffic, the method is reported to achieve the lowest MAE and RMSE among 18 baselines on most datasets, with error reductions exceeding 75% in some cases. The paper acknowledges weaker results on two natural-law datasets (Sunspot and US Births) and on the multivariate NN5 banking dataset, attributing this to limited long-term memory.","pith_inferences":["If the global-position allocation is what delivers the gains, the same fuzzified embedding could be applied to other sequence tasks (e.g., anomaly detection or classification) that currently rely on local convolutions only.","The PCP alignment is the testable hinge: a reader could re-run the pipeline with fixed one-sided padding and compare errors; if results change little, the claim rests mainly on the convolution design rather than the alignment.","The reported advantage over transformer-based baselines suggests CNN-style models with global context injection may be a competitive lightweight alternative for long-horizon forecasting, but that reading goes beyond the paper's own claims.","The parameter study indicates performance saturates around window sizes 10–12 for some datasets and keeps improving to 20 for others, implying the optimal window depends on data frequency and domain."],"forward_implications":["On most of 43 benchmark datasets the method reports lower MAE and RMSE than all 18 baselines, with error reductions over 75% in some cases.","The method performs particularly well on M-series and KDD Cup datasets, while the paper reports weaker results on Sunspot and US Births, which it attributes to limited capture of long-term natural-law patterns.","Larger sliding window sizes continue to reduce prediction error on some datasets (e.g., NN5 Daily and KDD Cup), supporting the claim that the global-context allocation improves long-range modeling.","The architecture's variable horizontal and vertical filter lengths let the model construct sub-windows within existing sliding windows, giving a fine-grained multi-scale representation that the paper says drives the gains."],"supporting_citations":[{"why":"Chen's fuzzy time series construction provides the baseline fuzzification scheme the paper improves by adding global position and tendency information.","marker":"[66]"},{"why":"Asymmetric convolution decomposition into horizontal and vertical filters is the basis for the partially asymmetric convolution and gives the complexity reduction.","marker":"[60]"},{"why":"Atrous (dilated) convolution underlies the bilateral atrous algorithm used to thin the fuzzified representation while enlarging the receptive field.","marker":"[61]"},{"why":"Res2Net's multi-branch, residual-like design (average pooling plus 1×1 convolution) is referenced for the second branch of the partially asymmetric block.","marker":"[76]"},{"why":"PFSD supplies the comparative error values for the five methods marked with • that are missing on some datasets.","marker":"[95]"},{"why":"Monash time-series forecasting archive provides the 43 benchmark datasets used in the experiments.","marker":"[78]"}],"fun_headline_variants":["Fuzzy-CNN hybrid tops most of 43 time-series datasets","Adaptive fuzzy time series with asymmetric conv beats benchmarks","Sliding-window fusion gives each time point global context","Atrous convolution cuts compute while preserving global features"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole approach depends on the padding-and-cropping step aligning every rebuilt element vector to the same shortest length while keeping each element's assigned fuzzy position and tendency value intact, so the tensors that enter the convolution stages are well-defined.","fun_headline_variants_meta":{"raw":{"variants":["Fuzzy-CNN hybrid tops most of 43 time-series datasets","Adaptive fuzzy time series with asymmetric conv beats benchmarks","Sliding-window fusion gives each time point global context","Atrous convolution cuts compute while preserving global features"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001154,"raw_usage":{"total_tokens":4796,"prompt_tokens":973,"completion_tokens":3823,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":589,"completion_tokens_details":{"reasoning_tokens":3757}},"tokens_in":589,"tokens_out":3823,"duration_ms":31308,"temperature":1.0,"reasoning_tokens":3757,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T13:24:47.027398+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Inspect Eq. (6) with $\\varphi = N$: the vector's right side starts at $\\alpha_r + (N+1)\\tau$, beyond the universe of discourse boundary $\\alpha_r$, so the padding and cropping described in Eq. (9) cannot be lossless for all elements simultaneously; computing the padded-cropped vectors for boundary elements would settle whether the fuzzified features are well-defined. A complementary statistical check is to replace the PCP with fixed one-sided padding and see whether the reported MAE/RMSE on the 43 datasets changes materially.","supporting_citations":[{"cited_title":"Chen, Forecasting enrollments based on fuzzy time series, Fuzzy Sets Syst","cited_arxiv_id":null,"evidence_quote":"Chen's fuzzy time series construction provides the baseline fuzzification scheme the paper improves by adding global position and tendency information."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Asymmetric convolution decomposition into horizontal and vertical filters is the basis for the partially asymmetric convolution and gives the complexity reduction."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Atrous (dilated) convolution underlies the bilateral atrous algorithm used to thin the fuzzified representation while enlarging the receptive field."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Res2Net's multi-branch, residual-like design (average pooling plus 1×1 convolution) is referenced for the second branch of the partially asymmetric block."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"PFSD supplies the comparative error values for the five methods marked with • that are missing on some datasets."},{"cited_title":"Godahewa, C","cited_arxiv_id":null,"evidence_quote":"Monash time-series forecasting archive provides the 43 benchmark datasets used in the experiments."}],"review_version":1}