Artificial Neural Network and Monte Carlo Simulation in a Hybrid Method for Time Series Forecasting with Generation of L-Scenarios

Akgün

Journal of Business Economics and Management

2019

Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Predicting Housing Sales in Turkey Using Arima, LSTM and Hybrid Models

Akgün

Journal of Business Economics and Management

2019

Robotics Transforming the Future

Educational bandwidth traffic prediction using non-linear autoregressive neural networks

Oumar¹,

Dyllon²,

Xiao³

et al. 2018

Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work on London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on Levenberg-Marquardt backpropagation algorithm. This technique can analyse and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques.

Wavelet Theory and Its Applications

Wavelet Transform for Educational Network Data Traffic Analysis

Dyllon¹,

Xiao²

2018

Network monitoring and analysis are very important, in order to understand the performance of the networks, the reliability of the networks, the security of the networks, and to identify potential problems. In this chapter, we present our latest work on university network data traffic analysis by using continuous wavelet transform (CWT). With CWT, you can analyse the data and show how the frequency content of the data changes over time. This time dependent frequency varying information, which is lacking in other techniques, such FFT, is very useful for network traffic analysis. A twelve month's network traffic data, include World Wide Web (WWW) data and Email data were presented in 3D format, by using wavelet transform, we can visualise the hourly, daily, weekly and monthly activities. We will first present the theoretical background, then show the experimental results.