Optimal starting point for time series forecasting

2024-09-25 · Yiming Zhong, Yinuo Ren, Guangyao Cao, Feng Li, Haobo Qi

Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.

📄 PDF Abstract BibTeX arXiv:2409.16843

Code (1)

feng-li/forecasting-with-optimal-starting-point 공식 구현

Tasks

Time SeriesTime Series Forecasting

Methods 이 논문이 사용한 방법론

BASE 설명 없음

Focus 설명 없음

Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization

2021-12-15 · Himanshi Charotia, Abhishek Garg, Gaurav Dhama, Naman Maheshwari

In the context of time series forecasting, it is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. However, choosing among different ensembles…

Demand Forecasting Model Selection Time Series Time Series Analysis+1

auto-sktime: Automated Time Series Forecasting

2023-12-13 · Marc-André Zöller, Marius Lindauer, Marco F. Huber

In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled with the expanding landscape of available f…

AutoML Bayesian Optimization Decision Making Time Series+1

PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning

2023-06-01 · Information Sciences 2023 6 · Manuel Jesús Jiménez Navarro, María Martínez Ballesteros, Francisco Martínez Álvarez, Gualberto Asencio Cortés

Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful to plan ahead, save time, resources, and help avoid undesired scenarios. To make t…

Time Series Time Series Forecasting Univariate Time Series Forecasting

Time series forecasting using neural networks

2014-01-07 · Bogdan Oancea, ŞTefan Cristian Ciucu

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for foreca…

General Classification Time Series Time Series Analysis Time Series Forecasting+1

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

2023-08-10 · Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen 외

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quanti…

AutoML Philosophy Probabilistic Time Series Forecasting Time Series+1