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Paper Citation Record · LEDGER

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

As of 16 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 0 inbound Pith citation observations for arXiv:1909.00590.

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

pith.paper-citation-record.v1
1909.00590 v5

Coverage vector

measured 100 of 103 reference resolution

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measured 100 of 100 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

100 of 103 outbound references displayed

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External citation measurements

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Outbound references

Observation 3389f7f7-d68e-487c-8e62-6ff1a23836fc · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 1

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This paper cites GluonTS: Probabilistic Time Series Models in Python.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions GluonTS: Probabilistic Time Series Models in Python

Reference 2

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 3

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This paper cites The tourism forecasting competition.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions The tourism forecasting competition

Reference 4

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This paper cites J., Song, H., Wu, D., 2010.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions J., Song, H., Wu, D., 2010

Reference 5

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This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Neural Machine Translation by Jointly Learning to Align and Translate

Reference 6

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This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 7

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Observation 2531c76b-7227-4999-a8f3-56cbc39dbe28 · outbound

This paper cites Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

Reference 8

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This paper cites Sales demand forecast in e-commerce using a long short-term memory neural network methodology.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Sales demand forecast in e-commerce using a long short-term memory neural network methodology

Reference 9

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This paper cites Learning Stochastic Recurrent Networks.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Learning Stochastic Recurrent Networks

Reference 10

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This paper cites A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition

Reference 11

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This paper cites J., Koo, B., 2018.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions J., Koo, B., 2018

Reference 12

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This paper cites Hyperopt: Distributed asynchronous hyper-parameter optimization.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Hyperopt: Distributed asynchronous hyper-parameter optimization

Reference 13

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Random search for Hyper-Parameter optimization

Reference 14

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

Reference 15

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Conditional Time Series Forecasting with Convolutional Neural Networks

Reference 16

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This paper cites Time Series Analysis: Forecasting and Control.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Time Series Analysis: Forecasting and Control

Reference 17

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 18

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Learning phrase representations using RNN Encoder--Decoder for statistical machine translation

Reference 19

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions G., Mirisaee, H., Goswami, P., Gaussier, E., A \"i t-Bachir, A., Strijov, V., 2017

Reference 20

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions B., Cleveland, W

Reference 23

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Capacity and trainability in recurrent neural networks

Reference 24

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions F., 2008

Reference 25

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions F., Hibon, M., Nikolopoulos, K., Jul

Reference 26

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions TensorFlow Distributions

Reference 27

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Adaptive subgradient methods for online learning and stochastic optimization

Reference 28

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions M3 user guide

Reference 30

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Bayesian optimization

Reference 31

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Regularization paths for generalized linear models via coordinate descent

Reference 32

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

Reference 33

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions S., Salinas, D., Flunkert, V., Januschowski, T., 16--18 Apr 2019

Reference 34

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Web traffic time series forecasting

Reference 35

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Deep residual learning for image recognition

Reference 36

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

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Observation f30c8bb3-ef5a-4f93-bca7-5ee1bc49cedb · outbound

This paper cites H., Leyton-Brown, K., 17--21 Jan 2011.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions H., Leyton-Brown, K., 17--21 Jan 2011

Reference 39

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Observation 1386e99e-8776-4afb-b47a-c771ce6527c5 · outbound

This paper cites A brief history of time series forecasting competitions.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A brief history of time series forecasting competitions

Reference 40

Resolution
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Observation 19c2d856-0199-43b5-8210-1d45b8f87dbd · outbound

This paper cites tsfeatures: Time Series Feature Extraction.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions tsfeatures: Time Series Feature Extraction

Reference 41

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.326522Z digest=sha256:954c07a3d4691ef57b227766e516d68fb6f1c13bd67293ba549952a9de4a4c22

Observation bd689cc5-6335-4507-a435-967f5a132cd0 · outbound

This paper cites Automatic time series forecasting: The forecast package for R.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Automatic time series forecasting: The forecast package for R

Reference 42

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.331133Z digest=sha256:f3ab972615a4d98a7269579040c95ddd7ab221b5f2670f21427ca19ef394c42f

Observation 8c7cb80f-af54-4990-bb17-6d0e73f09479 · outbound

This paper cites Forecasting with exponential smoothing.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Forecasting with exponential smoothing

Reference 43

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.335462Z digest=sha256:42cea0a2d88fd9c4389257950f92f2fbcfc1d07b2a2862a9c6eb2d59dc2404aa

Observation 3b90314e-7a1b-408b-a1ee-211c537ff929 · outbound

This paper cites J., Koehler, A.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions J., Koehler, A

Reference 44

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.339886Z digest=sha256:c3aa8dbe6d887e4c424ac7133f1edc47bb262a1fd7c9c36ff70792b2733e7db9

Observation 0595cb29-4b9b-4b62-948e-6a93bf231ac8 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 45

Resolution
verified fuzzy
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.344258Z digest=sha256:11d32aac3832e9222c8ee7dbf4be49222c2a3866898404f9ac4ccf9e95a48456

Observation 318794d3-f107-47e0-ac33-a6083c822d36 · outbound

This paper cites N., Yu, H., Jun.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions N., Yu, H., Jun

Reference 46

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.349627Z digest=sha256:6fcc078212a994143aaf5c8689a2765dbae48836a1949a6839d2c4d94808fcba

Observation 9cbe9500-a393-4a11-86b7-616358e7795b · outbound

This paper cites Criteria for classifying forecasting methods.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Criteria for classifying forecasting methods

Reference 47

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.354121Z digest=sha256:925894a4808ac7a16c01431afbc821468b75d783844321059630b0499d325714

Observation 00df0489-60b4-41b8-ab1d-e2e7e02c2e82 · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 48

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.358540Z digest=sha256:050eea04daa4a140afe1b1c217ec6eeded8c8d5037856be2a1d1a1d6bbfe10b1

Observation 5b8ec997-59bc-4aaf-86df-8f3ed600e4df · outbound

This paper cites An empirical exploration of recurrent network architectures.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions An empirical exploration of recurrent network architectures

Reference 49

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.362878Z digest=sha256:544decca1d0949a51d7abf922c6c7d8fbc7516c22743a7599ac92d713d02fbec

Observation d6f2cc9c-e671-466a-88d5-a1d82110d864 · outbound

This paper cites P., Ba, J., 7--9 May 2015.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions P., Ba, J., 7--9 May 2015

Reference 50

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.367484Z digest=sha256:3322e03b157e2be51dcd3b287d890498e8770628dc2076486e1eb398d0c478ec

Observation dbc918f2-8018-437b-a51d-6c7f12b945a8 · outbound

This paper cites A clockwork RNN.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A clockwork RNN

Reference 51

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.371952Z digest=sha256:3dd3f58fed39329c2c2195e4e6bc94b2cd78222c0086a150d904058a7dafa773

Observation 49ab0af2-a2ba-4479-84bc-93f60ef68905 · outbound

This paper cites Ensembles of recurrent neural networks for robust time series forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Ensembles of recurrent neural networks for robust time series forecasting

Reference 52

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.376255Z digest=sha256:476a5beb56dd64c848ab7ae3a48c7763047d9715036bf1e06ce6380a38b892e7

Observation 72f2c702-4b52-4dcb-9e37-72e477731f3f · outbound

This paper cites Modeling long- and Short-Term temporal patterns with deep neural networks.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Modeling long- and Short-Term temporal patterns with deep neural networks

Reference 53

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.380816Z digest=sha256:b8a791093b6dc834e6d76508955418608621ad5b6cca09089d8240cb1cf09bbc

Observation cf0d53ab-19d6-4917-8eb8-1b2429cf9271 · outbound

This paper cites E., Smyl, S., 06--11 Aug 2017.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions E., Smyl, S., 06--11 Aug 2017

Reference 54

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.385386Z digest=sha256:5b3b71014703e31ff86a382d623dc969625da163b06eb9334104478370914f8d

Observation b7625cd7-413b-44ab-b478-b029aee81eb4 · outbound

This paper cites Geoman: Multi-level attention networks for geo-sensory time series prediction.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Geoman: Multi-level attention networks for geo-sensory time series prediction

Reference 55

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.389872Z digest=sha256:f4fd514c373fd87074140683db0f686b02c9e543941f21d237d0a2ed7271e23f

Observation d48c2b29-ebac-4daf-a622-8b5a69109939 · outbound

This paper cites Smac v3: Algorithm configuration in python.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Smac v3: Algorithm configuration in python

Reference 56

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation a93d7c43-6549-4c6d-b3fe-c30a62724264 · outbound

This paper cites D., Sep 19--21 2015.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions D., Sep 19--21 2015

Reference 57

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation be2291fa-2c79-43b5-a5a2-8b873af20f93 · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 58

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation d23de0b5-a910-40ba-bf04-c15038cdcca2 · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 59

Resolution
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Observation e937f016-f240-466b-97e0-28165e85c249 · outbound

This paper cites Statistical and machine learning forecasting methods: Concerns and ways forward.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Statistical and machine learning forecasting methods: Concerns and ways forward

Reference 60

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e53b55f3-5e4a-4029-be0d-71d41d9db1af · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 61

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Observation e515191c-cdfd-4bb7-9edb-22413f68719e · outbound

This paper cites J., Talagala, T.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions J., Talagala, T

Reference 62

Resolution
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Source-reported events for the cited work

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Observation a021e68a-7db7-4f4b-87c2-0b35841b6c81 · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 63

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 69628f18-bf9a-4b08-84bc-ab5a8b8095bc · outbound

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 64

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f5d7b28a-1f0c-4907-90da-32392860f513 · outbound

This paper cites Training deep networks without learning rates through coin betting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Training deep networks without learning rates through coin betting

Reference 65

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.434411Z digest=sha256:bcf25cd2bfe552b21f5b11d2351338bdd618a6d4dae2838921e4fe564542a970

Observation 2e0913e4-b500-4ac4-81db-f5c671fd02d4 · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 66

Resolution
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source=arxiv_source observed=2026-08-14T05:52:12.439084Z digest=sha256:32988e24882da64e51a2c51ab9c25e258efdafc1b4cc68b4f970e061c105bbf8

Observation 67e78f67-d386-4f23-8717-68bcf0f9eb52 · outbound

This paper cites Multi-step-ahead host load prediction with GRU based Encoder-Decoder in cloud computing.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Multi-step-ahead host load prediction with GRU based Encoder-Decoder in cloud computing

Reference 67

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.443832Z digest=sha256:2c819d5461cc181e9349db55e7735a994c16ca0b10b917ca227ee1d59018641a

Observation e253a5df-6117-4204-b718-1868e8c12fc7 · outbound

This paper cites W., 2017.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions W., 2017

Reference 68

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.448316Z digest=sha256:a215219b22e15160bbe00df911445ec8e83c1d4316e4cb8f2c80cbb8969a974e

Observation 7aad99c7-78b0-4ef3-94af-14996af3fbe1 · outbound

This paper cites R: A Language and Environment for Statistical Computing.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions R: A Language and Environment for Statistical Computing

Reference 69

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.452619Z digest=sha256:81eeff2d20cc6165ce448ad4ddb19e55f063e41b2a1e5f0b120a69f7cd88db34

Observation d32e4c8c-6bfa-4ac9-9eae-30b49bf58730 · outbound

This paper cites M., Islam, M.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions M., Islam, M

Reference 70

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.456986Z digest=sha256:4ef907b0ba2cb4c41f2149df7dd428672f30afb87d9f22f601f5b1f4ff54e162

Observation 1f42fc2b-1c4b-4d91-8ef4-0074f17c02b0 · outbound

This paper cites S., Seeger, M., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T., 2018.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions S., Seeger, M., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T., 2018

Reference 71

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.461383Z digest=sha256:1bf091182d02aab4991e255a21d7588dfa52ead0e65d48b713fc58e624fb0dd1

Observation c2c60c24-5b01-4b33-a711-e341b4945c74 · outbound

This paper cites A., 2018.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A., 2018

Reference 72

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.465972Z digest=sha256:f05003d47084a8eaa0d6897302de1cc628d20df06afc2f14811b7a4dfad27d09

Observation e508ac18-26a4-4753-97dc-68db6752f17a · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.224069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.470329Z digest=sha256:5796be25af527572deeeb130d946a692224ef4a410cbb17f20e5b5dee4593f35

Observation 42e5095b-a395-4369-88f7-39bd9cae4895 · outbound

This paper cites M., Zimmermann, H.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions M., Zimmermann, H

Reference 74

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.475067Z digest=sha256:cef649419c1b2b65d470e768a4a17f46453c09229823277fdbaf4010cfa044be

Observation d75f3eeb-53f4-4d40-815e-407faa162c3d · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 75

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:52:13.195682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.479439Z digest=sha256:a8eac843f0b29b59ad627bb924897b14e74e009bdfe5701191a7197b11fc12a6

Observation dac15eb6-647d-422a-89b4-7922115c5e98 · outbound

This paper cites Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-14T05:52:12.483854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:52:12.483854Z digest=sha256:9c2d3f226ab8a29fce50a3b2326f603fcb797c2745aebf170494e097edcf76ea

Observation 806cec29-cda0-45a9-a0e2-d7a17a04d4d2 · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 77

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:52:13.180763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.488539Z digest=sha256:375ec79331a3da9eb54c383b85f0e28df8c5fb993458bfeaab22a79a2bd0aa7c

Observation 50553b87-d2bb-4952-90a7-7f1a305c5591 · outbound

This paper cites Temporal pattern attention for multivariate time series forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Temporal pattern attention for multivariate time series forecasting

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.166864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.493076Z digest=sha256:eead85d77ea0878b44b90f095fb20ca80486b478d63d4cd226f727875c964a68

Observation 9eccceba-9ce8-4b04-ae26-74eaffef566e · outbound

This paper cites Forecasting short time series with LSTM neural networks.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Forecasting short time series with LSTM neural networks

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.151170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.497459Z digest=sha256:2ce8ce783d1858d04cdbc337b5c3a42d6933fd28f269f90e51e1fa14c6d49f27

Observation bd4156c5-2913-474f-a30d-9dac2f5f1d45 · outbound

This paper cites Ensemble of specialized neural networks for time series forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Ensemble of specialized neural networks for time series forecasting

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.136379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.501854Z digest=sha256:bf129a2e45b7f72ff3bc44948c1021a0f28a80f52eb5a6812e139644754d654a

Observation 22652682-dbb5-4c9d-b71e-18b1d3004ee8 · outbound

This paper cites A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.120775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.506151Z digest=sha256:058b082deb2b283b597995bd42c701d0cc677a7bda0f90931575648a02bcbd65

Observation 97ceff26-389a-4b42-b95b-f959ffa893b4 · outbound

This paper cites Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.105816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.510859Z digest=sha256:0bf888c0c9ec45efec7f87d39a8f3811300c3fb1f630e6de5d9c57304bd29441

Observation b4916b3f-a58f-49fc-98ce-d08b12dbbdf5 · outbound

This paper cites Spearmint.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Spearmint

Reference 83

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.515327Z digest=sha256:31ee7a815497f7817d77cf23137eb0a87d93ca0abc1b159f9ef1bf608b118406

Observation e16acabb-d952-451b-b290-d32c945b6166 · outbound

This paper cites P., 03--08 Dec 2012.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions P., 03--08 Dec 2012

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.077149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.519714Z digest=sha256:248f8567feedbf58b426f7cf024d4a17f2e864a40dc80b7cc3a295bf7924bc1d

Observation 7a486bee-4625-4fd6-9a8e-5b3700413d8c · outbound

This paper cites S., Gunasekar, S., Srebro, N., Jan.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions S., Gunasekar, S., Srebro, N., Jan

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.062565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.524214Z digest=sha256:0feddad23376ceeffe86d7e6b1d065b44c0ac3edb2361f8fb1a85303ae09aee6

Observation 9a48d22c-e57a-48e0-865a-7f1c24f64148 · outbound

This paper cites On the results and observations of the time series forecasting competition cif 2016.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions On the results and observations of the time series forecasting competition cif 2016

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.048248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.528700Z digest=sha256:e40c8c34e3183765af645e4689fb333c1704048c9ee3b1810a6794f2fd9cebdf

Observation 0051b8c8-fda7-47e8-87bf-12b2c2b385da · outbound

This paper cites kaggle-web-traffic.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions kaggle-web-traffic

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.032824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.533213Z digest=sha256:9ec0021be42ae919707c31232aad38b56c1b52d0d60b205ade26243d7edca103

Observation ac8c5667-69d8-4282-aa12-5f419daad2d9 · outbound

This paper cites V., 08--13 Dec 2014.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions V., 08--13 Dec 2014

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:13.017972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.537659Z digest=sha256:11c123a867216195f8b597b0630305294dfffdc5c6700f1170945f1a50ef89ea

Observation 96ed6f1a-2c4d-420e-9607-170c3d2a6513 · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 89

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:52:13.001383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.542264Z digest=sha256:93f2bef10305574ece924c48f0a482a11d6811378dce994b4776e41bbe42b10b

Observation bc4bd3df-b869-4eb1-b50b-5c42f045c6db · outbound

This paper cites R., Kourentzes, N., Fildes, R., Feb 2015.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions R., Kourentzes, N., Fildes, R., Feb 2015

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.986310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.547032Z digest=sha256:3ea078202f829cdab08a5b6f1085d9cffb17abed5dcb1792cc4b9b6c9286cd40

Observation 18498fd4-a9a0-40c2-a5ba-e0c7c8944fc7 · outbound

This paper cites W., Kavukcuoglu, K., Sep 13-15 2016.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions W., Kavukcuoglu, K., Sep 13-15 2016

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.971292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.551601Z digest=sha256:46b2ce01831c7734c68b9c0e622aad23ca2f49b4b13acc3090b627800cea7531

Observation 9a222bed-e953-4c7f-86a6-ef9ab173cbd5 · outbound

This paper cites Deep factors for forecasting.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Deep factors for forecasting

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.956245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.556081Z digest=sha256:b07b8a8b2ccdfdf6bbae9e17a58870080f4bfc27fafba08422432a76b6bef0a1

Observation 26bbe2c0-c727-45a1-8be5-f8f2f9ffbd77 · outbound

This paper cites A Multi-Horizon quantile recurrent forecaster.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A Multi-Horizon quantile recurrent forecaster

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.941150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.560486Z digest=sha256:45698af39079ceddfda8599ba9a23966c4df4a49199d765f2c01b93451be2f8e

Observation a0edd32d-2e6d-4ed7-93ce-3efdd973e7c0 · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 94

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:52:12.925811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.565105Z digest=sha256:83bd476803a487855744afd0f5c7be7a36d54aa71a8c5fb3f98d9f572ce81068

Observation d3a21aa5-91cc-4da9-9f37-257f775043a2 · outbound

This paper cites rBayesianOptimization: Bayesian Optimization of Hyperparameters.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions rBayesianOptimization: Bayesian Optimization of Hyperparameters

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.909690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.569589Z digest=sha256:828c7a42841d5ca5f1ca7e5a341ca3de6a9690f92fa178f1ddb74cfb3b11da98

Observation db84c9e7-a193-466b-b90e-e3d96db51c17 · outbound

This paper cites Depth-Gated LSTM.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Depth-Gated LSTM

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.893409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.574031Z digest=sha256:b32c413c37d28b37d7aca8f6fed6b7f2fd2833dc2de5e14c96d471272f5b1786

Observation d5992a94-ce40-4e67-8cb5-50f91426f391 · outbound

This paper cites A., 2000.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions A., 2000

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.878171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.578410Z digest=sha256:0bc1327b27b4d7c6d3f616e2834439d8a6982222a59846caaaa8f043a9eca792

Observation 7b0cdb15-5c85-475b-8ef2-6575a94a58cb · outbound

This paper cites Y., 1998.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Y., 1998

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.862160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.582925Z digest=sha256:2f71466667d6116dad1269f4ee412c084943b826ad88678204e36618f7b0c009

Observation 09cedcf3-6240-422a-9da2-d9ca4ead23ba · outbound

This paper cites an unresolved cited work.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Unresolved cited work

Reference 99

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:52:12.844703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.587354Z digest=sha256:89d79c8028f064f60ff2e91c7b2660f7a82bf7dd09805411ba2615de81dd3110

Observation 7d51c2f3-02b0-4582-8a42-27f952fa7bb2 · outbound

This paper cites P., Berardi, V.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions P., Berardi, V

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:52:12.828992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T05:52:12.591858Z digest=sha256:0faaa9c9d5ae454e5f585d00880c1df7025a8c5884b895a408047a3d8407e816

Pith citing papers

No inbound Pith citation observations are available.