Paper Citation Record · LEDGER

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2510.04487.

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

pith.paper-citation-record.v1
2510.04487 v5

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:31:39.944872Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:10:12.434970Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:11:15.634485Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation f1620966-6835-4ccc-88ad-4094c3d1c6bd · outbound

This paper cites Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Tarkmen, and Yuyang Wang.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Tarkmen, and Yuyang Wang

Reference 1

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Observation 7730d5e7-f727-4e86-a558-75da0b3591a8 · outbound

This paper cites Maddix, Michael W.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Maddix, Michael W

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:34.995094Z

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Observation 5228ec8b-a662-4f0e-9b98-fffc48e4ff8d · outbound

This paper cites Hyndman, Haiyan Song, and Doris C.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Hyndman, Haiyan Song, and Doris C

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.063460Z

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Observation efce0775-3f19-4c64-b828-1cbfdd178147 · outbound

This paper cites Neural machine translation by jointly learning to align and translate.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Neural machine translation by jointly learning to align and translate

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.126402Z

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Observation 8db473fd-fdf7-4f45-84c4-fbbbf427e1ed · outbound

This paper cites PyTorchForecasting: Forecasting with neural networks made simple.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility PyTorchForecasting: Forecasting with neural networks made simple

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.169899Z

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Observation 993609ee-2ebe-4fa9-89c6-c82c24619122 · outbound

This paper cites A neural probabilistic language model.Journal of Machine Learning Research, 3:1137–1155, 2003.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility A neural probabilistic language model.Journal of Machine Learning Research, 3:1137–1155, 2003

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.232751Z

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Observation de580170-7225-46d9-8ddd-5473c182b3c2 · outbound

This paper cites On the use of cross-validation for time series predictor evaluation.Information Sciences, 191:192–213, 2012.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility On the use of cross-validation for time series predictor evaluation.Information Sciences, 191:192–213, 2012

Reference 7

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Observation ef061b2f-166f-42a8-83f0-e81316b1d60d · outbound

This paper cites Language models are few-shot learners.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Language models are few-shot learners

Reference 8

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Observation f16f3ca2-65f0-4d9d-a138-d920bc9500cb · outbound

This paper cites Olivares, Boris N.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares, Boris N

Reference 9

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Observation d8de52ba-ded3-4ab7-b3b7-d965d1df2cf1 · outbound

This paper cites Dilated recurrent neural networks.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Dilated recurrent neural networks

Reference 10

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Observation 9d8069e5-24fd-4db2-97d1-178d108b8c98 · outbound

This paper cites Chen, Lee Dicker, Carson Eisenach, and Dhruv Madeka.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Chen, Lee Dicker, Carson Eisenach, and Dhruv Madeka

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.548376Z

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Observation 12453f54-946c-41ea-a24c-bf4e1b408008 · outbound

This paper cites Dai and Quoc V.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Dai and Quoc V

Reference 12

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Observation 5659f7e1-c7d4-45b9-ac58-b91b20bffadf · outbound

This paper cites A decoder-only foundation model for time-series forecasting, 2024.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility A decoder-only foundation model for time-series forecasting, 2024

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.767222Z

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Observation 61d12861-c61d-4ecb-b79b-3d65036cd249 · outbound

This paper cites MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention

Reference 14

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Observation a405f083-950a-446b-899c-759d28d65792 · outbound

This paper cites Foster and Robert A.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Foster and Robert A

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:35.940339Z

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Observation 1daa30d3-90c7-4417-a018-ce55ab9b21c4 · outbound

This paper cites Timegpt, 2023.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Timegpt, 2023

Reference 16

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Observation 9e6d6dc0-93b1-4e75-9f4e-12462df0ef9d · outbound

This paper cites Olivares.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares

Reference 17

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Observation 4d3d4d47-0600-45c5-aa6c-dd4ed8b5f81d · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Strictly proper scoring rules, prediction, and estimation

Reference 18

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Observation b9623eab-3589-4fe4-91e5-27ad013a9042 · outbound

This paper cites an unresolved cited work.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:36.419237Z

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Observation c495bab9-f1ae-4e31-a360-3cb93b34ee5d · outbound

This paper cites HEATH and PETER L.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility HEATH and PETER L

Reference 20

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Observation 35da161e-150a-471a-8ea3-bbed9f6fab85 · outbound

This paper cites Darts: User-friendly modern machine learning for time series.Journal of Machine Learning Research, 23(124):1–6, 2022.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Darts: User-friendly modern machine learning for time series.Journal of Machine Learning Research, 23(124):1–6, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:36.644744Z

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Observation fd52fd6b-2037-487c-abb4-415cc55f1698 · outbound

This paper cites Forecasting seasonals and trends by exponentially weighted moving averages.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Forecasting seasonals and trends by exponentially weighted moving averages

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:36.771047Z

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Observation 3efa3d65-7921-4035-af85-46e18742cf3e · outbound

This paper cites Olivares.Forecasting: Principles and Practice, the Pythonic Way.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares.Forecasting: Principles and Practice, the Pythonic Way

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:36.831476Z

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Observation 90e47e61-a93c-49aa-a397-5f2d9fd9de11 · outbound

This paper cites Hyndman and Baki Billah.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Hyndman and Baki Billah

Reference 24

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Observation 2601c702-32ad-420a-b62b-64934cc435f3 · outbound

This paper cites Hyndman and Yeasmin Khandakar.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Hyndman and Yeasmin Khandakar

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:36.953221Z

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Observation 30f7ecaa-799f-4667-a38d-5cea796feb47 · outbound

This paper cites Hyndman and Anne B.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Hyndman and Anne B

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.019126Z

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Observation 943c5551-9d4f-4c42-88cf-d9ed0666c6a6 · outbound

This paper cites Regression quantiles.Econometrica, 46(1):33–50, 1978.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Regression quantiles.Econometrica, 46(1):33–50, 1978

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.078626Z

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Observation 39072e85-4193-427c-b863-f3b0f6d31f69 · outbound

This paper cites Deeply- Supervised Nets.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Deeply- Supervised Nets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.145994Z

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Observation c1266e91-0527-4dd8-93d6-92dfe7df9597 · outbound

This paper cites Arık, Nicolas Loeff, and Tomas Pfister.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Arık, Nicolas Loeff, and Tomas Pfister

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.202330Z

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Observation f6954e4d-6a44-4079-aa64-f5c5a030e6ba · outbound

This paper cites Kale, Charles Elkan, and Randall C.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Kale, Charles Elkan, and Randall C

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.324438Z

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Observation 0ea8dab6-4a5d-444a-a2a2-32c21265e96a · outbound

This paper cites Makridakis, A.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Makridakis, A

Reference 31

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Observation cf7904ef-aa19-41a5-b67c-c2f9b14be5a6 · outbound

This paper cites The M3-competition: results, conclusions and implica- tions.International Journal of Forecasting, 16(4):451–476, 2000.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility The M3-competition: results, conclusions and implica- tions.International Journal of Forecasting, 16(4):451–476, 2000

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.528172Z

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Observation 98d46605-826d-47b5-af32-7822531f7044 · outbound

This paper cites The M4 competition: 100,000 time series and 61 forecasting methods.International Journal of Forecasting, 36(1):54– 74, 2020.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility The M4 competition: 100,000 time series and 61 forecasting methods.International Journal of Forecasting, 36(1):54– 74, 2020

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.594818Z

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Observation 72167770-fbe3-45e6-bdb7-d1a4ca597153 · outbound

This paper cites Marshall and Ingram Olkin.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Marshall and Ingram Olkin

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.669475Z

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Observation 7ba28fe5-c134-494a-941c-c3133c1e3924 · outbound

This paper cites Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:37.804940Z

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Observation ecf2ce4d-4c56-4970-a0b7-dcfeac9ed571 · outbound

This paper cites Olivares, Cristian Challú, Federico Garza, Max Mergenthaler Canseco, and Artur Dubrawski.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares, Cristian Challú, Federico Garza, Max Mergenthaler Canseco, and Artur Dubrawski

Reference 36

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Observation 5d8a6a88-098c-44a1-b336-c7b17fcfbcb6 · outbound

This paper cites Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures

Reference 37

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This paper cites Olivares, Malcolm Wolff, Tatiana Konstantinova, Shankar Ramasubramanian, Boris Ore- shkin, Andrew Gordon Wilson, Andres Potapczynski, Willa Potosnak, Mengfei Cao, Michael W.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares, Malcolm Wolff, Tatiana Konstantinova, Shankar Ramasubramanian, Boris Ore- shkin, Andrew Gordon Wilson, Andres Potapczynski, Willa Potosnak, Mengfei Cao, Michael W

Reference 38

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Observation 45a2e34a-56e0-4afe-82e5-bb9d9a4c522e · outbound

This paper cites Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio

Reference 39

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Observation c087ea52-a1d5-4087-8880-2e868002dcca · outbound

This paper cites Improving language understanding by generative pre-training.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Improving language understanding by generative pre-training

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:38.341301Z

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Observation 8c9aea27-84a6-4431-9abd-711bfc4a3de9 · outbound

This paper cites DeepAR: Probabilistic forecasting with autoregressive recurrent networks.International Journal of Forecasting, 36(3):1181–1191, 2020.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility DeepAR: Probabilistic forecasting with autoregressive recurrent networks.International Journal of Forecasting, 36(3):1181–1191, 2020

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:38.476641Z

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Observation c78f9bee-1e8a-4cfb-a2b8-e3eb4b0a5e53 · outbound

This paper cites Investigating the accuracy of cross-learning time series forecasting methods.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Investigating the accuracy of cross-learning time series forecasting methods

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:38.675805Z

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Observation 86f85455-a32d-42b8-8d12-0350afe4b264 · outbound

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Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Shumway and D.S

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:38.776646Z

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Observation 553c0d8c-8325-45d4-824c-62e5bf8a73da · outbound

This paper cites A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting.International Journal of Forecasting, 07 2019.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting.International Journal of Forecasting, 07 2019

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:38.940736Z

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Observation 9971f793-69a3-418c-863a-c1fcb7574175 · outbound

This paper cites On the categorization of demand patterns.Journal of the Operational Research Society, 56, 05 2005.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility On the categorization of demand patterns.Journal of the Operational Research Society, 56, 05 2005

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.072524Z

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Observation 3eaa9b24-1116-46b9-b6ca-bca768a969fd · outbound

This paper cites Beril Toktay and Lawrence M.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Beril Toktay and Lawrence M

Reference 46

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Observation 8ba07d77-b5f3-473d-8cc1-ec1666c637e9 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility WaveNet: A Generative Model for Raw Audio

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.335437Z

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Observation 35585375-1abe-4986-8799-43674a5c5432 · outbound

This paper cites A Multi- horizon Quantile Recurrent Forecaster.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility A Multi- horizon Quantile Recurrent Forecaster

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.455120Z

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Observation 613ddd68-f448-4837-a45f-0bc4b7954a3b · outbound

This paper cites Olivares, Boris Oreshkin, Sunny Ruan, Sitan Yang, Abhinav Katoch, Shankar Ramasubramanian, Youxin Zhang, Michael W.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Olivares, Boris Oreshkin, Sunny Ruan, Sitan Yang, Abhinav Katoch, Shankar Ramasubramanian, Youxin Zhang, Michael W

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.560676Z

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Observation 478788dc-7776-4d7b-97ab-40861b580f6a · outbound

This paper cites Unified training of universal time series forecasting transformers, 2024.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Unified training of universal time series forecasting transformers, 2024

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.666283Z

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Observation 3545d004-2f32-4ea1-8d8f-920a13903a57 · outbound

This paper cites Time Series Library (TSLib).

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Time Series Library (TSLib)

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T11:31:39.800449Z

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Observation a0051742-f563-45f2-bf58-237783ee38da · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 52

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Pith citing papers

Observation a0dbf7ef-bef2-43bf-9938-68c53ea92417 · inbound

MICA: Multivariate Infini Compressive Attention for Time Series Forecasting cites this paper.

MICA: Multivariate Infini Compressive Attention for Time Series Forecasting Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-13T01:17:47.918879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f78ffd1-8b1d-4b44-87bc-50c36d5d2f25 · inbound

MICA: Multivariate Infini Compressive Attention for Time Series Forecasting cites this paper.

MICA: Multivariate Infini Compressive Attention for Time Series Forecasting Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-13T01:17:47.918879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.