Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers
Time series forecasting has attracted significant attention, leading to the de-velopment of a wide range of approaches, from traditional statistical meth-ods to advanced deep learning models. Among them, the Auto-Regressive Integrated Moving Average (ARIMA) model remains a widely adopted linear technique due to its effectiveness in modeling temporal dependencies in economic, industrial, and social data. On the other hand, polynomial classifi-ers offer a robust framework for capturing non-linear relationships and have demonstrated competitive performance in domains such as stock price pre-diction. In this study, we propose a hybrid forecasting approach that inte-grates the ARIMA model with a polynomial classifier to leverage the com-plementary strengths of both models. The hybrid method is evaluated on multiple real-world time series datasets spanning diverse domains. Perfor-mance is assessed based on forecasting accuracy and computational effi-ciency. Experimental results reveal that the proposed hybrid model consist-ently outperforms the individual models in terms of prediction accuracy, al-beit with a modest increase in execution time.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series ForecastingSimilar Papers 제목 키워드 기반
Anticipating dengue outbreaks using a novel hybrid ARIMA-ARNN model with exogenous variables
Dengue incidence forecasting using hybrid models has been surging in the data rich world. Hybridization of statistical time series forecasting models and machine learning models are explored for dengue forecasting with d…
Time SeriesTime Series ForecastingQuantum-classical hybrid models based on error correction for time series forecasting
Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concu…
Time Series ForecastingImproving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlin…
Time SeriesTime Series AnalysisTime Series ForecastingParallelTime: Dynamically Weighting the Balance of Short- and Long-Term Temporal Dependencies
Modern multivariate time series forecasting primarily relies on two architectures: the Transformer with attention mechanism and Mamba. In natural language processing, an approach has been used that combines local window …
Multivariate Time Series ForecastingMulti-Task Time Series Forecasting With Shared Attention
Time series forecasting is a key component in many industrial and business decision processes and recurrent neural network (RNN) based models have achieved impressive progress on various time series forecasting tasks. Ho…
Time SeriesTime Series AnalysisTime Series Forecasting