Stock Price Prediction Based on ARIMA-RNN Combined Model

Yu, ShuiLing; Li, Zhe

doi:10.12783/dtssehs/icss2017/19384

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, linear prediction models cannot capture the nonlinearity of electricity usage and other factors [27]. Therefore, several nonlinear models such as artificial neural networks [28], Gaussian process regression [29], recurrent neural networks (RNNs) [30] and LSTM [31] have been studied over the past decade to accommodate the nonlinearity of data in a hybrid forecasting model. In a hybrid forecasting structure, linear and nonlinear forecasting models are combined as follows:…”

Section: B Hybrid Stlf Approachesmentioning

confidence: 99%

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

2019

View full text Add to dashboard Cite

In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting scheme is proposed for a mixed-use complex. The aggregated load of the complex is composed of the mixtures of different electricity usage patterns, and short-term load forecasting can be implemented by summing disaggregated sub-load predictions. However, tracing all usage patterns of sub-loads for prediction may be infeasible because of limited resources for measurement and analysis. To prevent this infeasibility, the proposed scheme focuses on effective decomposition using the sub-loads of typical characteristic load profiles and their representative pilot signals. Separate forecasts are obtained for the decomposed characteristic sub-loads using a hybrid scheme, which combines day-type conditioned linear prediction with long short-term memory regressions. Complex campus load data are considered to evaluate the proposed CLD-based hybrid forecasting. The evaluation results show that the proposed scheme outperforms conventional hybrid or similar-day-based forecasting approaches. Even when sub-load measurements are available only for a limited period, the CLD scheme can be applied for the extended training data through virtual disaggregations. INDEX TERMS Day-ahead load forecasting, time series analysis, long short-term memory, hybrid forecasting model, characteristic load decomposition, hierarchical load forecasting.

show abstract

Section: B Hybrid Stlf Approachesmentioning

confidence: 99%

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

2019

View full text Add to dashboard Cite

show abstract

“…Su 13 employed stochastic recurrent neural networks to capture the normal pattern of multivariate time series by modeling data distribution through random latent variables. YU et al 14 built a hybrid stock prediction model using ARIMA and RNN. WANG et al 15 proposed an RC neural network with savings pool structure to predict chaotic sequence migration.…”

Section: Introductionmentioning

confidence: 99%

A new method of financial multivariate time series forecasting based on complex network attention mechanism

Xiong,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

Due to the high degree of correlation and interaction among multiple financial series, as well as the non-stationary and nonlinear characteristics of these series, predicting financial multivariate time series accurately using traditional time series analysis methods is challenging. However, neural network models can effectively handle nonlinearity and instability, enabling relatively accurate predictions. In this paper, we propose a novel method called MTP-CVGAT(Multivariate Time-Series Prediction via Correlation and Visible Graphs Attention Network), which combines the correlation and visibility features of multivariate series. Taking into account accommodation and catering stocks of companies listed on China's Shanghai-Shenzhen A-share market as an example, our results demonstrate that the prediction performance of MTP-CVGAT surpasses that of other models including Long short-term memory neural network model(LSTM), multi-variable time series anomaly detection model based on graph attention network, and transfer learning model based on Wang X G et al.'s approach.

show abstract

A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models

Thangarajan,

Nagaraja,

Dhandra

2023

Trends in Mathematics

View full text Add to dashboard Cite

Stock Price Prediction Based on ARIMA-RNN Combined Model

Cited by 5 publications

References 12 publications

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

A new method of financial multivariate time series forecasting based on complex network attention mechanism

A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models

Contact Info

Product

Resources

About