Paper Citation Record · LEDGER

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting

As of 8 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2608.01857.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.01857 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:31:42.155985Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3cde5670-a297-4d63-b40a-841bf2bfdca6 · outbound

This paper cites Fastdifferentiablesortingandranking.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting Fastdifferentiablesortingandranking

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.138930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa8ef2f5-1fe1-49cf-ac3f-ca119866a93e · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.382301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f24d879-1f07-49d9-9e42-4b675dcfddea · outbound

This paper cites SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.456689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0b70025-ff31-4259-ac8b-0d346cbf4b9c · outbound

This paper cites an unresolved cited work.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:42.155985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd13ea74-326c-4c0b-9a4e-cc123483ca25 · outbound

This paper cites InInternational Conference on Learning Representations (ICLR).

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting InInternational Conference on Learning Representations (ICLR)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.910980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc8d30ec-a5e0-4399-a936-498d4a1da368 · outbound

This paper cites It improves DA over the baseline but, unlike CosDir, it op- timizes a hard up/down label that ignores how strongly the trajectories agree (Table 7).

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting It improves DA over the baseline but, unlike CosDir, it op- timizes a hard up/down label that ignores how strongly the trajectories agree (Table 7)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:42.014164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986f5404-6f72-4710-8376-c324c90463c1 · outbound

This paper cites 2023), it reports the change in DA and MSE of each auxiliary loss relative to the base loss.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting 2023), it reports the change in DA and MSE of each auxiliary loss relative to the base loss

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:42.062759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbfb54c7-1d9c-4c30-8e96-6e36c807b9d2 · outbound

This paper cites Jiang,W.2021.Applicationsofdeeplearninginstockmarket prediction: Recent progress.Expert Systems with Applica- tions, 184: 115537.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting Jiang,W.2021.Applicationsofdeeplearninginstockmarket prediction: Recent progress.Expert Systems with Applica- tions, 184: 115537

Reference 1970

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.246688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f2d4f72-aa65-4a84-9739-d9e806fb3729 · outbound

This paper cites InAdvances in Neural Information Processing Systems (NeurIPS).

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting InAdvances in Neural Information Processing Systems (NeurIPS)

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.664313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22a5002a-3ffa-44d4-9da6-f79207d582c4 · outbound

This paper cites LeGuen,V.;andThome,N.2019.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting LeGuen,V.;andThome,N.2019

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.331356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53afc1a3-05c3-4bc8-a469-e635cee06aa1 · outbound

This paper cites Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.808372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a18a2d5-18f2-4094-b614-139cb5c8a7a7 · outbound

This paper cites predict,then optimize.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting predict,then optimize

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.192761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 519f8b02-158a-48b3-a0df-15b8270f5a02 · outbound

This paper cites InInternational Confer- ence on Learning Representations (ICLR).

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting InInternational Confer- ence on Learning Representations (ICLR)

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.567795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95d050bb-a6b2-48d2-8e4b-818dcab78fc3 · outbound

This paper cites CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting.

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T19:31:41.735567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.