Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm

Liu, Rui; Liu, Lu

doi:10.1007/s00500-018-03739-w

Cited by 43 publications

(54 citation statements)

References 28 publications

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“…The problem of time series prediction is considered as the most important problem in machine learning, with a large number of practical applications such as stock price trend prediction [ 39 ], housing price prediction [ 40 ], sensor data analysis [ 41 ], and water price prediction [ 42 ]. LSTMs are the most popular specialized model of recurrent neural networks (RNNs) for the time series forecasting problem.…”

Section: Related Workmentioning

confidence: 99%

“…These gates have the ability to capture the temporal changes for extremely long sequential data. Because of its advantages, it has been utilized widely in various applications such as text [ 43 ], videos [ 44 ], time series analysis [ 39 , 40 ], traffic forecast [ 45 ], speech recognition [ 46 ], and time series anomaly detection [ 47 ]. Lin et al [ 43 ] introduce an application of LSTMs on the task of mention extraction, where LSTMs extract and classify overlapped and nested structure mentions.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, Ta et al [ 39 ] utilize LSTMs to predict stock movement based on historical data. Recently, Liu et al [ 40 ] developed an LSTM approach that incorporated a modified genetic algorithm with multi-level probability crossover to select appropriate features and the optimal hyper-parameters to predict the housing price of a city by using historical data. This model was verified on a housing price dataset in Shenzhen, China.…”

Section: Related Workmentioning

confidence: 99%

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Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Kieu

et al. 2020

Sensors

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Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Kieu

et al. 2020

Sensors

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“…Within this context, the LSTM (long short-term memory) model holds a special position in that it produces extremely good results in the area of dynamic counting, which is typical, for example, for current electricity distribution networks, as well as for the economic environment [16]. It is for this reason that deep learning LSTM models are used, for example, by predictive models intended for high-frequency trading in financial markets [17] or for the prediction of future trends and the prices of housing [18]. The application of LSTM simply produces better results than vector regression or back propagation neural networks.…”

Section: Introductionmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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“…High housing price will cause social con icts, and the Chinese government has launched a series of measures to limit housing price [4]. For the government to formulate more accurate measures of housing price, a long short-term memory approach is proposed to predict the housing price [5].…”

Section: Introductionmentioning

confidence: 99%

Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

Zhang

Chen

2019

Complexity

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To analyze the relationship between air quality index (AQI) and housing price, six relationship indexes between air quality index and housing price were calculated using grey spectrum theory, specifically grey association spectrum, grey cospectrum, grey amplitude spectrum, grey phase spectrum, grey lag time length, and grey condense spectrum. Three main change periods were extracted. There was a negative correction between the air quality and the housing price in Handan. The results provide a basis for the government’s measures to prevent haze.

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Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm

Cited by 43 publications

References 28 publications

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

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