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Boosted Embeddings for Time Series Forecasting

2021-04-10 · Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr

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.

📄 PDF Abstract BibTeX arXiv:2104.04781

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Tasks

Deep LearningTime SeriesTime Series Analysis Time Series Forecasting

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음

Sigmoid Activation 설명 없음

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Seq2Seq Seq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one…

Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting

2022-03-02 · Grzegorz Dudek

Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble le…

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Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning

2019-09-17 · Long H. Nguyen, Zhenhe Pan, Opeyemi Openiyi, Hashim Abu-gellban 외

A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption. To improve forecasting performance, traditiona…

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Forecasting with Hyper-Trees

2024-05-13 · Alexander März, Kashif Rasul

We introduce the concept of Hyper-Trees and offer a new direction in applying tree-based models to time series data. Unlike conventional applications of decision trees that forecast time series directly, Hyper-Trees are …

Inductive Bias Time Series Time Series Forecasting

Explainable boosted linear regression for time series forecasting

2020-09-18 · Igor Ilic, Berk Gorgulu, Mucahit Cevik, Mustafa Gokce Baydogan

Time series forecasting involves collecting and analyzing past observations to develop a model to extrapolate such observations into the future. Forecasting of future events is important in many fields to support decisio…

Decision Making Prediction Intervals regression Time Series+2

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting

2025-08-30 · Shiqiao Zhou, Holger Schöner, Huanbo Lyu, Edouard Fouché 외 arxiv

Time series forecasting is a long-standing and highly challenging research topic. Recently, driven by the rise of large language models (LLMs), research has increasingly shifted from purely time series methods toward har…

Time Series Forecasting