Local Wheat Price Prediction Models

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16040035 ◽

2020 ◽

Vol 16 (4) ◽

pp. 584-601

Author(s):

Chunwei Chang ◽

Shengli Li

Keyword(s):

Prediction Models ◽

Interaction Effects ◽

Sharing Economy ◽

Tourist Attractions ◽

Price Prediction ◽

Price Determinants

This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants.

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Short-Term Stock Price Prediction Models Based on Economic Background

Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering ◽

10.1145/3452940.3452947 ◽

2020 ◽

Author(s):

Ruien Luo

Keyword(s):

Stock Price ◽

Prediction Models ◽

Short Term ◽

Stock Price Prediction ◽

Price Prediction ◽

Economic Background

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A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Mathematics ◽

10.3390/math7100898 ◽

2019 ◽

Vol 7 (10) ◽

pp. 898 ◽

Cited By ~ 12

Author(s):

Suhwan Ji ◽

Jongmin Kim ◽

Hyeonseung Im

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Learning Methods ◽

The Public ◽

Price Prediction ◽

Profitability Analysis ◽

The Media ◽

Long Short Term Memory

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v3i2.65 ◽

2020 ◽

Vol 3 (2) ◽

pp. 54-59

Author(s):

Marco Febriadi Kokasih ◽

Adi Suryaputra Paramita

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Gradient Boosting ◽

Fair Price ◽

Price Prediction ◽

Online Marketplace ◽

Extreme Gradient Boosting ◽

Property Owners ◽

Boosting Algorithm

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

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Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1084.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 542-546

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Prediction Models ◽

Learning Algorithms ◽

Housing Price ◽

Machine Learning Algorithms ◽

Selling Price ◽

Prediction Problem ◽

Real Dataset ◽

Price Prediction

This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.

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House Price Prediction Using Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7849.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 717-722 ◽

Cited By ~ 2

Keyword(s):

Machine Learning ◽

Real Estate ◽

Prediction Models ◽

Learning Algorithm ◽

Housing Cost ◽

Prediction Methods ◽

Machine Learning Algorithm ◽

Lasso Regression ◽

Cost Prediction ◽

Price Prediction

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction that tend to the advancement of real estate policies schemes. This study utilizes machine learning algorithms as a research method that develops housing price prediction models. We create a housing cost prediction model In view of machine learning algorithm models for example, XGBoost, lasso regression and neural system on look at their order precision execution. We in that point recommend a housing cost prediction model to support a house vender or a real estate agent for better information based on the valuation of house. Those examinations exhibit that lasso regression algorithm, in view of accuracy, reliably outperforms alternate models in the execution of housing cost prediction.

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Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

Applied Intelligence ◽

10.1007/s10489-021-02602-0 ◽

2021 ◽

Author(s):

Zhichao Zhao ◽

Jinguo You ◽

Guoyu Gan ◽

Xiaowu Li ◽

Jiaman Ding

Keyword(s):

Prediction Models ◽

Moving Average ◽

Attention Mechanism ◽

Civil Aviation ◽

Data Sets ◽

Prediction System ◽

Price Prediction ◽

Dual Stage ◽

Core Facilities ◽

The Impact

AbstractAirfare price prediction is one of the core facilities of the decision support system in civil aviation, which includes departure time, days of purchase in advance and flight airline. The traditional airfare price prediction system is limited by the nonlinear interrelationship of multiple factors and fails to deal with the impact of different time steps, resulting in low prediction accuracy. To address these challenges, this paper proposes a novel civil airline fare prediction system with a Multi-Attribute Dual-stage Attention (MADA) mechanism integrating different types of data extracted from the same dimension. In this method, the Seq2Seq model is used to add attention mechanisms to both the encoder and the decoder. The encoder attention mechanism extracts multi-attribute data from time series, which are optimized and filtered by the temporal attention mechanism in the decoder to capture the complex time dependence of the ticket price sequence. Extensive experiments with actual civil aviation data sets were performed, and the results suggested that MADA outperforms airfare prediction models based on the Auto-Regressive Integrated Moving Average (ARIMA), random forest, or deep learning models in MSE, RMSE, and MAE indicators. And from the results of a large amount of experimental data, it is proven that the prediction results of the MADA model proposed in this paper on different routes are at least 2.3% better than the other compared models.

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Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models

Vestnik MEI ◽

10.24160/1993-6982-2020-6-119-128 ◽

2020 ◽

Vol 6 (6) ◽

pp. 119-128

Author(s):

Anna V. Shikhina ◽

◽

Tatyana V. Yagodkina ◽

Keyword(s):

Electricity Markets ◽

Prediction Accuracy ◽

Regression Models ◽

Prediction Models ◽

Free Market ◽

Moving Average ◽

Market Price ◽

Electricity Price ◽

Multifactor Models ◽

Price Prediction

The solution of problems concerned with predicting a free market price for electricity through constructing different prediction models is considered. In so doing, a shift is made from an analysis of conventional regression and auto-regression models of the moving average to the proposed combined multifactor models, which also include the time trend and dummy variables. This shift is partly justified by the specific behavior of the electricity price in the free market, which is caused by a strictly cyclic change of its value, e.g., proceeding from such attributes as the heating season, day of week, etc. The techniques of constructing combined prediction models has been developed to the level of elaborating effective computational procedures based on the Statistica and OsiSoft PI-System software packages. The application of the autoregressive and combined regression prediction models to the Russian market has demonstrated their fairly good effectiveness with an acceptable level of accuracy. A comparison of the achieved levels of accuracy provided by the competing models has not shown any advantages of the shift to the use of combined regression multifactor models in terms of achieving better prediction accuracy; however, their application for analyzing the influence of different factors on the predicted variable may become a fundamental advantage in selecting the type of prediction model. Despite their being limited to an analysis of the Belgorod region market, the obtained results demonstrate the achieved prediction accuracy that is as least as good, and in the main is even better than the majority of the data presented in the review of the results for European electricity markets. The article substantiates the advisability of studying the combined regression models as a tool for analyzing the influence of individual factors as components influencing the electricity price formation for the predicted period, given that the accuracy level of the combined regression models corresponds to the currently achieved electricity price prediction accuracy levels.

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