A LSTM and Graph CNN Combined Network for Community House Price Forecasting

Ge, Chuancai

doi:10.1109/mdm.2019.00-15

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Analysed the characteristics of each cluster to identify spatial patterns, groupings, and trends within the Visakhapatnam housing market. Examined cluster centroids and cluster assignments to understand the distribution of properties across different market segments [13].…”

Section: K-means Clusteringmentioning

confidence: 99%

Accurate House Price Projections

Turpati,

Krishna,

Rout

2024

Int. J. Res. Publ. Rev.

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This study delves into the dynamics of housing prices in Visakhapatnam, employing a multifaceted approach that combines linear regression modelling, geocoding, and K-means clustering. Leveraging a comprehensive dataset encompassing diverse locations, area (in square feet), and bedroom-bathroom configurations, we construct a robust linear regression model for price estimation. The dataset is further enriched through geocoding, introducing latitude and longitude coordinates to each location. Expanding our analysis, we employ K-means clustering to unveil spatial patterns and groupings within the housing market. Our findings reveal intricate relationships between location, property attributes, and housing prices, providing valuable insights for both prospective buyers and real estate stakeholders.Visualizations on the Visakhapatnam map illustrate the spatial distribution of housing clusters, shedding light on localized market trends. This integrated approach not only refines price predictions but also enhances our understanding of the spatial dynamics shaping the real estate landscape in Visakhapatnam. The outcomes of this study contribute to informed decision-making in the housing market and set the stage for future research exploring the nuances of regional real estate dynamics.

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Section: K-means Clusteringmentioning

confidence: 99%

Accurate House Price Projections

Turpati,

Krishna,

Rout

2024

Int. J. Res. Publ. Rev.

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“…An updated version of the CNN algorithm, the CNN-LSTM model, where the convolutional layers are followed by the LSTM network, provided a good forecasting performance for car sales prediction in Ou-Yang et al (2022). Similarly, the CNN and LSTM combined model was used for house price forecasting (Ge, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Algorithm Forecasting the Unemployment Rates in the Central European Countries

Madaras

2024

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The aim of this paper is to forecast the monthly unemployment rate’s time series using deep learning algorithms. Based on data from five Central European countries, we tested the forecasting performance of the ‘conventional’ Box–Jenkins methodology in comparison with three deep learning models: the CNN (Convolutional Neural Network), the MLP (Multilayer Perceptron) and the random forest algorithm. The MAPE, MAE, RRMSE, and MSE error tests were used for testing the forecasting results. In our results, the ARIMA model was outperformed by one of the deep learning algorithms in all cases. The medium-term predictions suggest that in the Central European area, unemployment will remain relatively high in the future.

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“…This technique reduces the costs of sorting, making it less likely to overfit. The mathematical formulae for LightGBM is presented from ( 7) to (10):…”

Section: Lightgbmmentioning

confidence: 99%

“…Unlike other data types, real estate data also has a clear spatial attribute. Previous research has incorporated spatial dependency analysis into housing price prediction models, such as considering community factors or adding geographical analysis [9][10][11]. Comparative studies on the prediction models of house prices with complex attributes are still lacking.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Real Estate Price Prediction Based on LSTM and LGBM

Shi¹

2023

HSET

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Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.

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A LSTM and Graph CNN Combined Network for Community House Price Forecasting

Cited by 8 publications

References 4 publications

Accurate House Price Projections

Accurate House Price Projections

Deep Learning Algorithm Forecasting the Unemployment Rates in the Central European Countries

Comparison of Real Estate Price Prediction Based on LSTM and LGBM

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