Forecasting stock closing prices with an application to airline company data

Xu, Xu; Zhang, Yixiang; McGrory, Clare Anne; Wu, Jinran; Wang, You-Gan

doi:10.1016/j.dsm.2023.09.005

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time series models arrange data chronologically and leverage the presumed repetition of patterns from past periods into the present and future. The purpose of time series model analysis is to uncover patterns for modeling future events (Alyousifi et al, 2021 ), identifying a variety of patterns that show to be influential on the response variable (Wu et al, 2023 ), enhancing forecasting accuracy and stability from the perspectives of noise distribution and outliers (Yang et al, 2023 ), and exploring how a particular model selection can be better applied to forecast the new data in the future (Xu et al, 2023 ). Based on the abundant data and its diverse attributes consisting of trends, seasonality, and cyclicality, current values are often modeled based on past data exhibiting inter-variable correlations, commonly through linear or nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Arif,

Herlinawati,

Devianto

et al. 2024

Front. Big Data

View full text Add to dashboard Cite

Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Arif,

Herlinawati,

Devianto

et al. 2024

Front. Big Data

View full text Add to dashboard Cite

show abstract

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions

Shamshad,

Ullah,

Ullah

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA's (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.

show abstract

Forecasting stock closing prices with an application to airline company data

Cited by 2 publications

References 32 publications

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions

Contact Info

Product

Resources

About