Jiang, Xudong, Loo, Mingshan, Li, Wengen, Zhang, Mingrui, Guan, Jihong, Zhou, Shuigeng · Apr 25, 2026
AdaMamba adapts frequency information for long-term time series forecasting
Markdown · JSON · Original source
Authors Xudong Jiang and colleagues introduce AdaMamba, a model for long-term forecasting. They argue that existing frequency-aware methods often use fixed or manually chosen frequency components, while Mamba-based forecasters treat frequency information as auxiliary rather than connecting it to how the model updates its internal state. AdaMamba is designed to make that connection: one module learns relationships across variables and temporal scales, and an adaptive frequency-gated state space module generates frequency bases from the input context and uses them to modulate state evolution. In plain terms, the method aims to let changing patterns in the data influence how the model tracks information over . The authors report experiments on seven public and four domain-specific datasets, saying AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency. Escape Meridian interpretation: these results make the approach a candidate for forecasting settings such as energy scheduling or infrastructure capacity planning, which the abstract names as applications; the evidence here does not establish performance in operational use. The abstract does not give benchmark-level results or define what “competitive” efficiency means.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Apr 25, 2026 (2026-04-25).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T00:38:05.770Z. First observed: 2026-11-05T05:00:32.975Z. Retrieved: 2026-11-10T00:33:43.088Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The evidence is the abstract; it does not provide -level results, dataset names, or comparisons that would allow the reported performance claim to be assessed here. The abstract calls computational efficiency “competitive” but gives no efficiency measurements or comparison details. The abstract names application areas but reports no operational deployment or real-world outcomes.
Open question
What results on the individual and domain-specific datasets support the authors’ claim of consistent outperformance?
Source-stated
The authors propose AdaMamba to integrate input-dependent frequency information into modeling temporal dependencies.
we propose AdaMamba, a novel approach that explicitly internalizes input-dependent frequency information into the modeling of temporal dependencies.
Source-stated
AdaMamba’s interaction learning module captures cross-variable dependencies and multi-scale temporal context.
the former captures cross-variable dependencies and multi-scale temporal context
Source-stated
Its adaptive frequency-gated state space module generates context-dependent frequency bases and uses a time-frequency gate to modulate state evolution.
the latter generates context-dependent frequency bases and unifies temporal forgetting into a single time-frequency gate that directly modulates state evolution.
Source-stated
The authors report experiments on seven public long-term time series forecasting benchmarks and four domain-specific datasets, and claim consistent outperformance of state-of-the-art baselines with competitive computational efficiency.
Extensive experiments on seven public LTSF benchmarks and four domain-specific datasets demonstrate that AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency.
Escape Meridian interpretation
The abstract names personalized recommendations, energy scheduling, and infrastructure capacity planning as areas where time-series forecasting is used; it does not report deployment results for AdaMamba in these applications.
spanning personalized recommendations, energy scheduling, and infrastructure capacity planning.
Source and contributor context
Jiang, Xudong, Loo, Mingshan, Li, Wengen, Zhang, Mingrui, Guan, Jihong, Zhou, Shuigeng · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: f9702405-846f-452f-8b0b-461b557daf29.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T00:38:05.770Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 28a329dfb6cc37724b088780052de71d136cf3b6ad1ebaca7a89ca81c2369ceb. Source text hash: 1b530fab6369ba02ffe23e7b9e000f1f8486d180692272321eb8a9f8e72f3f4b.
Revision and source-check history
Version 1 · 2026-11-10T00:38:05.770Z
AdaMamba adapts frequency information for long-term time series forecasting
Authors Xudong Jiang and colleagues introduce AdaMamba, a model for long-term forecasting. They argue that existing frequency-aware methods often use fixed or manually chosen frequency components, while Mamba-based forecasters treat frequency information as auxiliary rather than connecting it to how the model updates its internal state. AdaMamba is designed to make that connection: one module learns relationships across variables and temporal scales, and an adaptive frequency-gated state space module generates frequency bases from the input context and uses them to modulate state evolution. In plain terms, the method aims to let changing patterns in the data influence how the model tracks information over . The authors report experiments on seven public and four domain-specific datasets, saying AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency. Escape Meridian interpretation: these results make the approach a candidate for forecasting settings such as energy scheduling or infrastructure capacity planning, which the abstract names as applications; the evidence here does not establish performance in operational use. The abstract does not give benchmark-level results or define what “competitive” efficiency means.
Original source
cs.AI · Article
Source details
Original source date: Apr 25, 2026 (2026-04-25).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T00:38:05.770Z. First observed: 2026-11-05T05:00:32.975Z. Retrieved: 2026-11-10T00:33:43.088Z.
Limits
The evidence is the abstract; it does not provide -level results, dataset names, or comparisons that would allow the reported performance claim to be assessed here. The abstract calls computational efficiency “competitive” but gives no efficiency measurements or comparison details. The abstract names application areas but reports no operational deployment or real-world outcomes.
Open question
What results on the individual and domain-specific datasets support the authors’ claim of consistent outperformance?
Source-stated
The authors propose AdaMamba to integrate input-dependent frequency information into modeling temporal dependencies.
we propose AdaMamba, a novel approach that explicitly internalizes input-dependent frequency information into the modeling of temporal dependencies.
Source-stated
AdaMamba’s interaction learning module captures cross-variable dependencies and multi-scale temporal context.
the former captures cross-variable dependencies and multi-scale temporal context
Source-stated
Its adaptive frequency-gated state space module generates context-dependent frequency bases and uses a time-frequency gate to modulate state evolution.
the latter generates context-dependent frequency bases and unifies temporal forgetting into a single time-frequency gate that directly modulates state evolution.
Source-stated
The authors report experiments on seven public long-term time series forecasting benchmarks and four domain-specific datasets, and claim consistent outperformance of state-of-the-art baselines with competitive computational efficiency.
Extensive experiments on seven public LTSF benchmarks and four domain-specific datasets demonstrate that AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency.
Escape Meridian interpretation
The abstract names personalized recommendations, energy scheduling, and infrastructure capacity planning as areas where time-series forecasting is used; it does not report deployment results for AdaMamba in these applications.
spanning personalized recommendations, energy scheduling, and infrastructure capacity planning.
Source and contributor context
Jiang, Xudong, Loo, Mingshan, Li, Wengen, Zhang, Mingrui, Guan, Jihong, Zhou, Shuigeng · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: f9702405-846f-452f-8b0b-461b557daf29.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T00:38:05.770Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 28a329dfb6cc37724b088780052de71d136cf3b6ad1ebaca7a89ca81c2369ceb. Source text hash: 1b530fab6369ba02ffe23e7b9e000f1f8486d180692272321eb8a9f8e72f3f4b.
passed · 2026-11-10T00:38:05.770Z · anthropic/claude-opus-5.5
Publication: f9702405-846f-452f-8b0b-461b557daf29
attribution — pass: The reading attributes AdaMamba and its performance claims to Xudong Jiang and colleagues, matching the listed authors. The outperformance and efficiency claims are presented as the authors' reports, not as established facts.
Authors:Xudong Jiang, Mingshan Loo, Wengen Li, Mingrui Zhang, Jihong Guan, Shuigeng Zhou
summary — pass: The title, summary and question match the abstract: the critique of fixed frequency heuristics and of Mamba treating frequency as auxiliary, the two modules, and the reported results. A plain-language sentence explains the core idea. Terms such as Mamba and state space can rely on tooltips. One small optional repair: mention the abstract's motivating 'time-frequency heterogeneity' point, that variables synchronized in time can differ in frequency. Its omission does not distort the reading. The question addresses a real gap.
variables that appear synchronized in the time domain can differ substantially in the frequency domain
date — pass: originalPublished 2026-04-25 matches the v1 submission date. It is not the 4 Nov 2026 revision or the feed date.
[Submitted on 25 Apr 2026 (v1), last revised 4 Nov 2026 (this version, v2)]
support — pass: The source excerpts support every claim. The application statement is labeled as Escape Meridian interpretation, stated conditionally, and explicitly says it does not show operational performance. The limitations about missing benchmark-level results and undefined 'competitive' efficiency are accurate to the abstract.
demonstrate that AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference