A novel hybrid model combining <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3056" altimg="si309.svg"><mml:mrow><mml:mi>β</mml:mi><mml:mi>S</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mi>M</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:math> and LSTM for time series forecasting

Kumar, B. Senthil; Sunil, .; Yadav, Neha

doi:10.1016/j.asoc.2023.110019

Cited by 18 publications

(3 citation statements)

References 38 publications

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“…The result showed that their model was more effective than other models for uncertain quantification. The typical statistical ARIMA was also combined with deep-learning methods for predicting the sunspot number, such as ARIMA-ANN [44], ARIMA-LSTM [45], and βSARMA-LSTM [46]. Panigrahi and Pattanayak et al [47] applied the hybridization of the ARIMA, exponential smoothing, and SVM to predict sunspot number time series.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Yang,

Liu,

Xuan

et al. 2024

Electronics

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To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Yang,

Liu,

Xuan

et al. 2024

Electronics

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“…Кроме указанных выше способов описания тренда, широко применяются математические модели временных рядов, построенные на основе нейронных сетей (LSTM 8 , GRU 9 и т.д.) [21][22][23][24][25]. Часто такие модели лучше описывают долговременные закономерности в данных по сравнению с моделями ARIMA, основанными на статистике [24,26].…”

Section: Introductionunclassified

“…Часто такие модели лучше описывают долговременные закономерности в данных по сравнению с моделями ARIMA, основанными на статистике [24,26]. Известны работы, где модели разных типов интегрируются в единый механизм [22,23]. Также в настоящее время исследователи делают попытки анализировать с помощью нейронных сетей характеристики и особенности временного ряда [26].…”

Section: Introductionunclassified

Analysis of approaches to identification of trend in the structure of the time series

Mokhnatkina,

Parfenov,

Petrusevich

2024

Rossijskij tehnologičeskij žurnal

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Objectives. The study set out to compare the forecasting quality of time series models that describe the trend in different ways and to form a conclusion about the applicability of each approach in describing the trend depending on the properties of the time series.Methods. A trend can be thought of as the tendency of a given quantity to increase or decrease over the long term. There is also an approach in which a trend is viewed as some function, reflecting patterns in the behavior of the time series. In this case, we discuss the patterns that characterize the behavior of the series for the entire period under consideration, rather than short-term features. The experimental part involves STL decomposition, construction of ARIMA models (one of the stages of preparation for which includes differentiation, i.e., removal of the trend and transition to a weakly stationary series), construction of ACD models (average conditional displacement) and other approaches. Time-series models based on various trend models are compared with respect to the value of the maximum likelihood function. Many of the combinations have not been constructed before (Fourier series as a trend model, combination of ACD model for trend with seasonal models). Example forecasts of macroeconomic statistics of the Russian Federation and stock prices of Sberbank on the Moscow Exchange in the time range of 2000–2021 are presented.Results. In the experiments, The LOESS method obtained the best results. A combination of polynomial model for trend description and ARIMA for seasonally description and combination of ACD algorithm for trend and ETS for seasonal model obtained good forecasts in case of seasonal time series, while Fourier time series as a trend model also achieved close quality of prediction.Conclusions. Since the LOESS method for groups of seasonal and non-seasonal series gives the best results for all indicators, this method can be recommended for obtaining the most accurate results for series of different nature. Trend modeling using Fourier series decomposition leads to quite accurate results for time series of different natures. For seasonal series, one of the best results is given by the combination of modeling a trend on the basis of a polynomial and seasonality in the form of the ARIMA model.

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Corrector LSTM: built-in training data correction for improved time-series forecasting

Baghoussi,

Soares,

Mendes-Moreira

2024

Neural Comput & Applic

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Traditional recurrent neural networks (RNNs) are essential for processing time-series data. However, they function as read-only models, lacking the ability to directly modify the data they learn from. In this study, we introduce the corrector long short-term memory (cLSTM), a Read & Write LSTM architecture that not only learns from the data but also dynamically adjusts it when necessary. The cLSTM model leverages two key components: (a) predicting LSTM’s cell states using Seasonal Autoregressive Integrated Moving Average (SARIMA) and (b) refining the training data based on discrepancies between actual and forecasted cell states. Our empirical validation demonstrates that cLSTM surpasses read-only LSTM models in forecasting accuracy across the Numenta Anomaly Benchmark (NAB) and M4 Competition datasets. Additionally, cLSTM exhibits superior performance in anomaly detection compared to hierarchical temporal memory (HTM) models.

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A novel hybrid model combining βSARMA and LSTM for time series forecasting

Cited by 18 publications

References 38 publications

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Analysis of approaches to identification of trend in the structure of the time series

Corrector LSTM: built-in training data correction for improved time-series forecasting

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