House Price Prediction Approach based on Deep Learning and ARIMA Model

Wang, Feng; Zhang, Haoyu

doi:10.1109/iccsnt47585.2019.8962443

Cited by 42 publications

(28 citation statements)

References 11 publications

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“…We developed a deep learning-based model inspired from [54]. Our choice for this architecture was motivated by its predictive performance on visual and textual features, addressed in many recent papers [55][56][57][58]. Our proposed model is a combination of a Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN).…”

Section: Proposed Modelmentioning

confidence: 99%

Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients

et al. 2020

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The limitations and high false-negative rates (30%) of COVID-19 test kits have been a prominent challenge during the 2020 coronavirus pandemic. Manufacturing those kits and performing the tests require extensive resources and time. Recent studies show that radiological images like chest X-rays can offer a more efficient solution and faster initial screening of COVID-19 patients. In this study, we develop a COVID-19 diagnosis model using Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) for mixed-data (numerical/categorical and image data). The model predicts and differentiates between COVID-19 and non-COVID-19 patients, such that early diagnosis of the virus can be initiated, leading to timely isolation and treatments to stop further spread of the disease. We also explore the benefits of using numerical/categorical data in association with chest X-ray images for screening COVID-19 patients considering both balanced and imbalanced datasets. Three different optimization algorithms are used and tested:adaptive learning rate optimization algorithm (Adam), stochastic gradient descent (Sgd), and root mean square propagation (Rmsprop). Preliminary computational results show that, on a balanced dataset, a model trained with Adam can distinguish between COVID-19 and non-COVID-19 patients with a higher accuracy of 96.3%. On the imbalanced dataset, the model trained with Rmsprop outperformed all other models by achieving an accuracy of 95.38%. Additionally, our proposed model outperformed selected existing deep learning models (considering only chest X-ray or CT scan images) by producing an overall average accuracy of 94.6% ± 3.42%.

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Section: Proposed Modelmentioning

confidence: 99%

Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients

et al. 2020

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“…Hence, it seems to be no significant difference in performance between the ARIMA model and the RNN [6]. This result is contrary to our initial expectation as it was anticipated that the RNN would be more efficient in processing sequential data, as shown in many comparative studies of model performance (Bin et al, 2017;Kong et al, 2017;Wang et al, 2019). This could be due to the insufficient amount of data used (only 175 periods).…”

Section: Resultsmentioning

confidence: 64%

“…The ARIMA model has been frequently used as a supplementary or competitive model in many comparative studies in promoting the deep learning approach (Yu et al, 2018;Tem€ ur et al, 2019;Wang et al, 2019). The ARIMA model is used in this study because it has demonstrated relatively good performances in a variety of previous studies, despite its simple parameterization of variables (Chen and Yu, 2010;Jadevicius and Huston, 2015;Hernandez-Matamoros et al, 2020).…”

Section: Arima Modelmentioning

confidence: 99%

“…In the 1990s and 2000s, the valuation approach from a spatial perspective has been suggested in various studies (Wiltshaw, 1996;Wyatt, 1996;McCluskey et al, 2000;Chica-Olmo, 2007) because location is the first and foremost price-forming factor, as evidenced in the 3L (location, location, location) slogan approach by Kiel and Zabel (2008). Since 2010, following the development of artificial intelligence techniques, researchers have attempted machine learningoriented valuations (Bin et al, 2017;Yu et al, 2018;Wang et al, 2019). The data science-based valuation model is still an emerging area in the real estate industry.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm

Lee

Park

2021

ECAM

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PurposeIt is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading volume at the local level is forecast through appropriate models to enhance the predictive accuracy.Design/methodology/approachFour representative housing submarkets in South Korea are selected, and their trading volumes are forecast. A well-established time-series model and a deep learning algorithm are employed: the autoregressive integrated moving average (ARIMA) model and the recurrent neural network (RNN), respectively. The trading volumes in adjacent areas are utilized as covariates, and an ensemble prediction is applied additionally to improve the model performance.FindingsThe results indicate no significant difference in prediction performance between the ARIMA model and the RNN, which can be attributed to the insufficient amount of data used. It is discovered that the spillover effects of trading volumes across the study areas can be exploited to improve the predictive accuracy, and that the diversity of the predicted values from the candidate models can be used to increase the forecasting accuracy further.Originality/valueWhereas property prices have been investigated extensively, the discussion on forecasting trading activity of properties is limited in the literature. The results of this study are expected to promote more interest in adopting a local perspective and using a diversity of predicted values when forecasting house trading volumes.

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“…House price prediction using DNN has only been recently used by few literature, despites its vital role in real estate planning and appraisal applications. Wang et al [10] adopted DNN model for house price prediction, where the results shown a good match between the predicted values and actual house prices. They highlighted the need for adaptation of non-traditional price prediction approaches with capacity for full utilization of existing big data.…”

Section: Introductionmentioning

confidence: 99%

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

Mostofi¹,

Toğan²,

Başağa³

2021

JCEMI

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High dimensionality and skewness are two intrinsic characteristics of real estate dataset that affects the price prediction accuracy of deep neural network (DNN). The objective of this study is to investigate the effect of skewness in prediction accuracy of combined principal component analysis (PCA) with DNN (PCA-DNN) model. This research follows a threefold approach over a high dimensional and positively skewed real estate price dataset. Firstly, data distribution is to conform with normality using three conventional skewness reduction techniques, namely as square root transformation (SRT), cube root transformation (CRT), and logarithmic transformation (LT) methods. Secondly, the high dimensionality of original, SRT, CRT and LT skewed datasets are to be reduced using PCA. Thirdly, price prediction accuracy of PCA-DNN model over datasets with different skewness levels are to be compared by observing their error values. The results suggest that CRT method can considerably improve both prediction accuracy and computational time of PCA-DNN model, while displaying a good generalization ability. Despite CRT method, SRT and LT methods resulted in high error values and overfitting issues, respectively.

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House Price Prediction Approach based on Deep Learning and ARIMA Model

Cited by 42 publications

References 11 publications

Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients

Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients

Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

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