Paper Citation Record · LEDGER

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.02606.

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

pith.paper-citation-record.v1
2505.02606 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:51:20.106521Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

34 of 34 outbound references displayed

verified exact1
verified fuzzy14
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 055743b5-2c34-467e-a08d-4e78393b875c · outbound

This paper cites Internet of Things applications: A systematic review.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Internet of Things applications: A systematic review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:21.026880Z

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Observation 132c30ce-6a99-436c-a7cb-6001a5b1fd9f · outbound

This paper cites Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:21.005763Z

Source-reported events for the cited work

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Observation 32062cc7-6d24-4207-b98d-cba16d4be7ba · outbound

This paper cites Challenges to the Reproducibility of Machine Learning Models in Health Care.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Challenges to the Reproducibility of Machine Learning Models in Health Care

Reference 3

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Observation bdad6a35-0c09-484d-b83a-b44364520663 · outbound

This paper cites Practical Recommendations for Gradient-Based Training of Deep Architectures.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Practical Recommendations for Gradient-Based Training of Deep Architectures

Reference 4

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Observation 69bbabe1-a9be-4fd1-a14b-28ee82e6d437 · outbound

This paper cites NHITS: Neural Hierarchical Interpolation for Time Series Forecasting.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting NHITS: Neural Hierarchical Interpolation for Time Series Forecasting

Reference 5

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Observation 1d19a1dd-5c8f-4f4e-aee7-341be7a9f9db · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting XGBoost: A Scalable Tree Boosting System

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:19.926862Z

Source-reported events for the cited work

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Observation d05d8261-d157-4710-9a52-910f2a696fee · outbound

This paper cites Time series compression survey.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Time series compression survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.983441Z

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Observation c6683da5-90ad-4379-afbc-23b064ca1d3c · outbound

This paper cites Biorthogonal bases of compactly supported wavelets.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Biorthogonal bases of compactly supported wavelets

Reference 8

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Observation 7fdefb0f-d35d-4423-b8bc-1acde17a4491 · outbound

This paper cites Day-ahead electricity price forecasting using the wavelet transform and ARIMA models.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.964327Z

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Observation 1b07aa17-d98a-40bd-8003-68974438a667 · outbound

This paper cites Lossy data compression for iot sensors: A review.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Lossy data compression for iot sensors: A review

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.946649Z

Source-reported events for the cited work

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Observation 7c692697-aad4-4c1c-976c-4a6c57baeb5a · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 11

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Observation e0db5472-a0d9-48c4-9e81-bc8446eb6a40 · outbound

This paper cites https://dlmf.nist.gov/, Release 1.2.4 of 2025-03-15.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting https://dlmf.nist.gov/, Release 1.2.4 of 2025-03-15

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.926533Z

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Observation 2fa91dc4-e53b-4cf9-b192-60175c822be5 · outbound

This paper cites An introduction to the Fourier transform: Relationship to MRI.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting An introduction to the Fourier transform: Relationship to MRI

Reference 13

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This paper cites http://www.deeplearningbook.org.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting http://www.deeplearningbook.org

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.905064Z

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Observation 30a2302e-90b5-44b5-8639-9622ad286ca4 · outbound

This paper cites Biosignal data augmentation based on generative adversar- ial networks.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Biosignal data augmentation based on generative adversar- ial networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.884192Z

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Observation 3bb81fb7-f36c-4446-8fd9-55414b29c545 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Deep Residual Learning for Image Recognition

Reference 16

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Observation 46b04395-d9ee-4ebe-b2e0-13bc53f6bc42 · outbound

This paper cites Darts: User-Friendly Modern Machine Learning for Time Series.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Darts: User-Friendly Modern Machine Learning for Time Series

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.865362Z

Source-reported events for the cited work

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Observation 6c198c75-61b6-444c-bfa5-2e205afb717f · outbound

This paper cites Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.845020Z

Source-reported events for the cited work

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Observation da32040e-f642-4065-9b7b-58d0d464b3ec · outbound

This paper cites Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables

Reference 19

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Observation 3f58fe78-5e84-4a4f-9bc3-7371bd603190 · outbound

This paper cites Sample Estimate of the Entropy of a Random Vector.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Sample Estimate of the Entropy of a Random Vector

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.825828Z

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Observation 7c36f762-d549-4045-8efe-9720e9adbec8 · outbound

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Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Estimating mutual information

Reference 21

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Observation d19ebb19-c91f-4476-a18e-36ccd43f450d · outbound

This paper cites Temporal Fusion Transformers for interpretable multi-horizon time series forecasting.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Reference 22

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Observation cb8b6081-45e1-4f9d-abc1-ef1823b92d47 · outbound

This paper cites Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:20.036041Z

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Observation 98627803-f3c4-4a73-8b0b-4ffe2164f824 · outbound

This paper cites Large text compression benchmark.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Large text compression benchmark

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.797612Z

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Observation e2bf4bc8-02ec-43e6-96c1-cb9ef9639b17 · outbound

This paper cites A theory for multiresolution signal decomposition: the wavelet representation.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting A theory for multiresolution signal decomposition: the wavelet representation

Reference 25

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Observation e5d90943-b039-40d9-a6d2-a9f35aedc0cd · outbound

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Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting A Wavelet Tour of Signal Processing

Reference 26

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Observation ffe2416b-bb68-4904-8649-30800a0520d4 · outbound

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Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Scikit-learn: Machine Learning in Python

Reference 27

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Observation 7eca6dba-784b-4461-8f42-0e975ff23f5f · outbound

This paper cites Mutual Information between Discrete and Continuous Data Sets.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Mutual Information between Discrete and Continuous Data Sets

Reference 28

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Observation 925f42e7-6837-4d5a-a2c7-b17a13d1d857 · outbound

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Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Reference 29

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Source-reported events for the cited work

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Observation 74e654b7-ee73-4017-83c5-d72df97e05a4 · outbound

This paper cites Financial time series forecasting with deep learning: A systematic literature review: 2005–2019.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Financial time series forecasting with deep learning: A systematic literature review: 2005–2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.744749Z

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Observation 3225e626-dbc1-4ec9-b2fe-c7261834c0da · outbound

This paper cites A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:20.081601Z

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

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

Reference 32

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Observation 446d99ea-fd67-4ca2-8ed8-e73a10d10ac8 · outbound

This paper cites Dropout: A Simple Way to Prevent Neural Networks from Overfitting.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:20.721176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c47e4a7-7765-4ddb-8d1e-0730dfd456fb · outbound

This paper cites Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network.

Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:20.106521Z

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Pith citing papers

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