A Hybrid System Based on Dynamic Selection for Time Series Forecasting

Oliveira, João Fausto Lorenzato de; Silva, Eraylson G.; Neto, Paulo S. G. de Mattos

doi:10.1109/tnnls.2021.3051384

Cited by 54 publications

(28 citation statements)

References 66 publications

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“…In general view, MPS (approaches based on ensemble) attained better results than single models. Tables 4, 5, and 6 show the superiority of these MPS based approaches and corroborate with literature findings [9], [12]- [14], [18] From the analysis of the accuracy of the MPS, it is possible to verify that DPS and DES approaches present higher variability in the accuracy than Ensembles. As all MPS used in this work employed the same pool, it can be inferred that this variability is related to how the forecasting models are chosen.…”

Section: A Discussionsupporting

confidence: 86%

“…The RoC is composed of the k patterns in the in-sample (training or validation sets) [12], which are more similar to the test pattern according to some measure such as the Euclidean distance [20]. This strategy for populating the RoC is applied for different tasks such as classification [21], [22], regression [23], [24] and time series forecasting [9], [12]- [14], [18].…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of dynamic selection approaches is directly related to the region of competence's quality, commonly defined using the k-nearest neighbors algorithm (k-NN) to find the k most similar patterns to the test pattern. However, to guarantee the optimum similarity measure is being adopted for a given time series is a challenging task [25] because the parameters setting of the k-NN is problem-dependent [18], [26]. Moreover, it is unlikely to assure the existence of patterns having similar behavior to the new test patterns (time windows) in the RoC due to noise, the available amount of data, or the absence of similar data [27].…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting

2021

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The development of accurate forecasting systems for real-world time series modeling is a challenging task. Due to the presence of temporal patterns that change over time, the adoption of a single model can lead to underperformed forecasts. In this scenario, Multiple Predictor Systems (MPS) emerge as an alternative to adopting single models since they struggle to learn in the presence of temporal patterns that change over time. Dynamic prediction/ensemble selection is a special case of MPS where each model is an expert in the time series's specific patterns. In dynamic selection, instead of combining all models, the most competent models per test pattern are selected. A criterion commonly used is to evaluate the models' performance in the region of competence, formed by the patterns present in the in-sample set (training or validation sets) more similar to the test pattern. Thus, the region of competence's quality is a key factor in the precision of the MPS. However, adequately defining the similarity criterion and the size of the region of competence is challenging and problem-dependent. Furthermore, there is no guarantee that similar data exist in the in-sample set. This paper proposes a dynamic selection approach entitled Dynamic Selection based on the Nearest Windows (DSNAW) that selects one or more competent models according to their performance in the region of competence composed of the nearest antecedent windows to the new target time window. This strategy assumes that the temporal windows closer to a test pattern have a behavior more similar to the target than in-sample data. The experimental study using ten well-known time series showed that the DSNAW outperforms the literature approaches.

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Section: A Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting

2021

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“…For the action recognition of human daily behavior, inertial sensors such as gyroscope and accelerometer are mainly used for algorithm classification and pattern recognition of daily behavior such as standing, walking, running, and lying [13]. Wang e basic idea of inertial sensor recognition is that athletes wear simple and light data collection sensors and send the collected data to the processing terminal [14] in real time to identify the athletes' posture according to various posture data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

Wang¹

2021

Mobile Information Systems

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Real-time collection of athletes’ abnormal training data can improve the training effect of athletes. This paper studies the real-time collection method of athletes’ abnormal training data based on machine learning. The main motivation of this paper is to collect the athletes’ abnormal training data in time, which can help to evaluate and improve the training effect. Four sensor nodes are arranged in the upper and lower limbs of athletes to collect the angular velocity, acceleration, and magnetic field strength data of athletes in training state. The data are sent to the data transmission base station through wireless sensors, and the data transmission base station transmits the data to the data processing terminal. The data processing terminal calculates the difference between the sample values of each sensor to obtain the data dispersion of each sensor. The features of each dimension data in a time domain and frequency domain are obtained by using the dispersion degree to construct 32-dimensional feature vectors, and the extracted feature vectors are input into the hidden Markov model. The forward algorithm is used to obtain the probability of the final observation sequence, so as to realize the final collection of athletes’ abnormal training data. The experimental results show that the accuracy and recall rate of the abnormal data collected by this method is higher than 98%, which requires less time.

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“…Sequential hybrid models have achieved promising results in the literature, however despite its assumption of decomposition of the time series in its linear and nonlinear counterparts, some problems remain unsolved. For instance, the residual data may present random fluctuations and heteroscedastic patterns, which can pose a challenge for the specification of nonlinear models [17], [18]. Ensemble methods can be employed to reduce the risk of selection of a misspecified model through the combination of several model.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Based Hybrid System for Residual Forecasting in Industrial Data

Vercosa¹,

Borba²,

Lima³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Time series forecasting has become an important task for several industrial processes. The employment of machine learning techniques also contributed to reduce costs and increase profits. In order to achieve accurate forecasts, it is important to perform a proper selection of parameters for the forecasting models. Hybrid sequential systems reduces model uncertainty through the employment of different models in the time series and residual series. In this work, an ensemble based hybrid sequential system is proposed, where a heterogeneous ensemble is used to perform residual forecasts. In this way, a pool of four models used in the industrial scenario are employed in order to achieve accurate forecasts. The experiments were conducted on real datasets from the industry in hourly averages and daily averages. The results show that the proposed system achieved promising results, and was able to improve the accuracy of the models in the pool in several cases.

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A Hybrid System Based on Dynamic Selection for Time Series Forecasting

Cited by 54 publications

References 66 publications

A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting

A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting

Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

An Ensemble Based Hybrid System for Residual Forecasting in Industrial Data

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