The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Prediction of House Price Using Stacked LSTM Model
Abstract
House price is used as an economic indicator. Prediction of house price is important as it can be helpful for those who want to buy or plan to buy a house in the future or near future. It can give them an idea of the future price. It is also be helpful for sellers who are willing to sell their house as they can understand the future trend of the price of their houses. House price prediction is beneficial for the investors who want to invest in real estate. It helps to know the future trends of the real estate of a locality. In this paper, we have used deep learning Long Short-term memory model to predict house price. This model performed quiet well. Here our main motive is to see how data size influences a deep learning model. In a single model we have used different data size (specifically 12 different data sizes) and compared the results. It is evident from the results that data size plays a significant role in our model’s performance.
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“The recom- mended approach performs more effectively than other benchmarks when compared to individual models, with a mean absolute percentage error (MAPE) of 17.83%.”
From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Goswami et al., 2022
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Research works printed but not linked: 9
Cited by
Saeed, N., Shafi, I., Pervez, S., Thompson, E. B., Castilla, A. K., Samad, M. A., & Ashraf, I. (2025) · Computational Economics · doi:10.1007/s10614-024-10837-5 · Cited by 8
Metadata
- Authors
- Goswami, S., Bramhe, V. S., & Khepra, S.
- Affiliations
- Sharda University,Department of Engineering and Technology,Greater Noida,India
- Venue
- 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)
- Publisher
- IEEE
- Published
- 2022-12-16
- Pages
- 778-783
- Cited by
- 3
- References
- 1
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- Goswami, S., Bramhe, V. S., & Khepra, S. (2022). Prediction of House Price Using Stacked LSTM Model. 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). https://doi.org/10.1109/icac3n56670.2022.10074011
