Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Borrero, Juan D.; Mariscal, Jesus

doi:10.3390/math10162915

Cited by 6 publications

(9 citation statements)

References 54 publications

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“…The Kalman filter has often been used to refine an ARIMA model by introducing a state-space system [29][30][31][32][33]. Despite the limited explicit references to the Kalman filter in the field of agri-food production [13], recent studies have utilized this technique for fitting predictive models within the horticultural sector [34].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In this study, we built on previous research [61,[94][95][96][97][98] to propose two hybrid forecasting models. The first model uses a novel approach of the Kalman filter [34] combined with a multilayer perceptron (MLP) neural network that has one hidden layer as its base architecture. The second model replaces NAR with SVR.…”

Section: Description Of the Hybrid Modelmentioning

confidence: 99%

“…The introduction of an ARIMA model into a state-space system, if it is carried out using the classical method [99,100], causes problems when the standard deviation of the white noise associated with the system is too large because the algorithm requires the introduction of a random value that can distort the result. In order to eliminate this potential problem, in this study, we followed a new method, namely, AKF [34], which eliminates the introduction of a random value generated by the machine and which can be defined by the equation of state (2), as follows:…”

Section: Description Of the Hybrid Modelmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model

Borrero,

Borrero-Domínguez

2023

Horticulturae

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This study presents a novel hybrid model that combines two different algorithms to increase the accuracy of short-term berry yield prediction using only previous yield data. The model integrates both autoregressive integrated moving average (ARIMA) with Kalman filter refinement and neural network techniques, specifically support vector regression (SVR), and nonlinear autoregressive (NAR) neural networks, to improve prediction accuracy by correcting the errors generated by the system. In order to enhance the prediction performance of the ARIMA model, an innovative method is introduced that reduces randomness and incorporates only observed variables and system errors into the state-space system. The results indicate that the proposed hybrid models exhibit greater accuracy in predicting weekly production, with a goodness-of-fit value above 0.95 and lower root mean square error (RMSE) and mean absolute error (MAE) values compared with non-hybrid models. The study highlights several implications, including the potential for small growers to use digital strategies that offer crop forecasts to increase sales and promote loyalty in relationships with large food retail chains. Additionally, accurate yield forecasting can help berry growers plan their production schedules and optimize resource use, leading to increased efficiency and profitability. The proposed model may serve as a valuable information source for European food retailers, enabling growers to form strategic alliances with their customers.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Description Of the Hybrid Modelmentioning

confidence: 99%

Section: Description Of the Hybrid Modelmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model

Borrero,

Borrero-Domínguez

2023

Horticulturae

Self Cite

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“…Next, we performed envelope extraction, extracting only the endpoints of the graph. After envelopment, the data were extracted and smoothed by applying the Kalman filter [20]. Comparing the data before and after smoothing, we found that the data before smoothing (Figure 6b) were more suitable for gait analysis and discrimination.…”

Section: Data Preprocessing For the Analysismentioning

confidence: 99%

Feature-Based Gait Pattern Modeling on a Treadmill

Shin,

Kim,

Han

et al. 2023

Electronics

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In this paper, we present a method of gait analysis on a treadmill based on pressure distribution. We aimed to model the gait patterns of a subject walking at a constant speed on a treadmill based on differences in current consumption. The changes in current consumption were converted into pressure distribution curves, and then specific features were extracted. The extracted features were used to model the walking pattern on a treadmill. To verify the validity of our proposed feature-based gait pattern modeling, we conducted experiments by gender, age, BMI (body mass index), and step-to-step symmetry. The experimental results showed that the heavier the subject, the higher the value of each feature. In particular, our feature point-based gait modeling provides an index that can help determine whether a subject’s gait is abnormal, depending on the difference between the features.

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“…Nonetheless, both the ARIMA and ARIMA-SVR models inherit limitations from the ARIMA framework, rendering them somewhat inflexible due to their reliance on linear relationships between observed variable values [28,29]. To overcome these limitations, the focus of autoregressive modeling has shifted towards neural networks in recent years [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks

Borrero,

Mariscal

2023

Algorithms

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Efforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning algorithms, has been explored to achieve accurate forecasts. The hybrid ARIMA-SVR model has garnered attention due to its fusion of a foundational linear model with error correction capabilities. However, its use is limited to stationary time series data, posing a significant challenge. To overcome these limitations and drive progress, we propose the innovative NAR–SVR hybrid method. Unlike its predecessor, this approach breaks free from stationarity and linearity constraints, leading to improved model performance solely through historical data exploitation. This advancement significantly reduces the time and computational resources needed for precise predictions, a critical factor in univariate economic time series forecasting. We apply the NAR–SVR hybrid model in three scenarios: Spanish berry daily yield data from 2018 to 2021, daily COVID-19 cases in three countries during 2020, and the daily Bitcoin price time series from 2015 to 2020. Through extensive comparative analyses with other time series prediction models, our results substantiate that our novel approach consistently outperforms its counterparts. By transcending stationarity and linearity limitations, our hybrid methodology establishes a new paradigm for univariate time series forecasting, revolutionizing the field and enhancing predictive capabilities across various domains as highlighted in this study.

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Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Cited by 6 publications

References 54 publications

Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model

Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model

Feature-Based Gait Pattern Modeling on a Treadmill

Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks

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