A hybrid modelling method for time series forecasting based on a linear regression model and deep learning

Xu, Wenquan; Peng, Hui; Zeng, Xiaoyong; Feng, Zhou; Tian, Xiaoying; Peng, Xiaoyan

doi:10.1007/s10489-019-01426-3

Cited by 74 publications

(32 citation statements)

References 42 publications

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“…Furthermore, ELM has fewer optimisation constraints [63], which has been shown to be an advantage in regression applications [64]. Although deep belief nets (DBN) has also been reported to have great performance [65] and it performed best in repetitions 30 and 6, its poor performance was also recognised. DBN had the maximum number of hyperparameters of the four algorithms examined in this study since a total of twelve hyperparameters (unit sizes of six layers, batch size, learning rate, number of epochs, rate of drop out and weight decay) had to be optimised.…”

Section: Performance Of Different Machine Learning Algorithmsmentioning

confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...

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Section: Performance Of Different Machine Learning Algorithmsmentioning

confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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“…In [11], a CNN-based framework for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL. A data fitting model combining linear regression and the deep belief network model has been proposed [12]. In addition, the long short-term memory network (LSTM) has special structure of memory and gate, which is also frequently used to solve problems of prediction [13]- [15].…”

Section: A Data Fitting Model Based On Intelligent Computing Methodsmentioning

confidence: 99%

A Genetic Programming-Driven Data Fitting Method

et al. 2020

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Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability. INDEX TERMS Data fitting, hybrid model, genetic programming, tree coding, interpretability.

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“…When they compared the performance of the proposed approach with the different models, they concluded that the DLSTM model performed better (Sagheer & Kotb, 2019). Xu et al (2019) have used a linear regression and deep learning hybrid model for time series estimation. They have concluded that the hybrid model has a higher estimation accuracy when compared to other models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They have concluded that the hybrid model has a higher estimation accuracy when compared to other models. Therefore, they have stated that the proposed hybrid model can be a useful tool for time series estimation (Xu et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Housing Sales in Turkey Using Arima, LSTM and Hybrid Models

Temür

Akgün

Temür

2019

Journal of Business Economics and Management

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Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.

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A hybrid modelling method for time series forecasting based on a linear regression model and deep learning

Cited by 74 publications

References 42 publications

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

A Genetic Programming-Driven Data Fitting Method

Predicting Housing Sales in Turkey Using Arima, LSTM and Hybrid Models

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