mlsanityData to 7 Oct 2026

Boosted Embeddings for Time Series Forecasting

arXiv 2104.04781 ↗First posted 10 Apr 2021cs.LG cs.AI stat.MLcited by 6

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Abstract (arXiv)

Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. In this paper, we propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs whose weights are incrementally found in a greedy manner over iterations. In particular, we develop a new embedding architecture that improves the performance of many deep learning models on time series using Gradient boosting variant. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 75 references that are in the map. First posted on arXiv 10 Apr 2021.

Builds on

20 of 75 references.

References · 20

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8 more
  1. 2019Time2Vec: Learning a Vector Representation of Timecited by 208
  2. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1.

6 papers.

  1. 2021Time Series Anomaly Detection with label-free Model Selectioncited by 3
  2. 2022Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodingscited by 3
  3. 2023Big Dataunicodex2013Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniquescited by 2
  4. 2021Real-time Drift Detection on Time-series Datacited by 1
  5. 2024Forecasting with Hyper-Treescited by 1
  6. 2023STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Predictioncited by 0