Forecasting Domestic Tourist Arrivals to Bali: H-WEMA Approach

Hansun, Seng; Kristanda, Marcel Bonar; Subanar, .; Indrati, Ch. Rini; Aryono, Teddy

doi:10.1109/conmedia46929.2019.8981825

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last one displays the level of inaccuracy in a percentage number, whereas the first two display the error level. As shown by Shahid et al in [37] and Hansun et al in [36,38], All three of those requirements can be stated as:…”

Section: Discussionmentioning

confidence: 99%

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

et al. 2023

View full text Add to dashboard Cite

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA,

show abstract

Section: Discussionmentioning

confidence: 99%