Improving exchange rate forecasting via a new deep multimodal fusion model

Windsor, Edmure; Cao, Wei

doi:10.1007/s10489-022-03342-5

Cited by 15 publications

(5 citation statements)

References 45 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [36].…”

Section: Resultsmentioning

confidence: 99%

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Purwinarko,

Amalia Langgundi

2023

J. Soft Comput. Explor.

View full text Add to dashboard Cite

Crude oil price fluctuations significantly affect commodity market price fluctuations, so a sudden drop in oil prices will cause a slowdown in the economy and other commodities. This is very important for Indonesia, one of the world's oil-producing countries, to gain multiple benefits from oil exports when world oil prices increase and increase economic growth. Therefore, a system is needed to predict world crude oil prices. In this case, the Particle Swarm Optimization (PSO) algorithm is applied as the optimization of the weight parameters in the Artificial Neural Network-Backpropagation (ANN-BP) method. We compared the ANN-BP–PSO and ANN-BP methods to obtain the method with the best causation value based on the MAPE and MSE results. PSO aims to find the best weight value by iterating the process of finding and increasing position, speed, Pbest, and Gbest until the iteration is complete. The results showed that the ANN-BP-PSO process was classified as very good and had a lower predictive error rate than the ANN-BP method based on the MAPE and MSE values, which is 5.02007% and 7.15827% compared to 6.28323% and 13.86345.

show abstract

“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [36].…”

Section: Resultsmentioning

confidence: 99%

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Purwinarko,

Amalia Langgundi

2023

J. Soft Comput. Explor.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [21].…”

Section: Resultsmentioning

confidence: 99%

Ensemble learning technique to improve breast cancer classification model

Dullah,

Apsari,

Jumanto

2023

J. Soft Comput. Explor.

View full text Add to dashboard Cite

Cancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method.

show abstract

“…Methodology Inspired by deep learning models in time series (Hu et al 2018;Hu and Zheng 2020;Windsor and Cao 2022), we implement a Temporal Attention LSTM with bidirectional encoder representations from transformers (BERT). As shown in Figure 4, the model learns temporally relevant information from sentences, emotions, and numerical data.…”

Section: Multivariate Time Seriesmentioning

confidence: 99%

StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series

Lee¹,

Youn²,

Poon³

et al. 2023

Preprint

View full text Add to dashboard Cite

There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10k English comments collected from StockTwits, a financial social media platform. Inspired by behavioral finance, it proposes 12 fine-grained emotion classes that span the roller coaster of investor emotion. Unlike existing financial sentiment datasets, StockEmotions presents granular features such as investor sentiment classes, fine-grained emotions, emojis, and time series data. To demonstrate the usability of the dataset, we perform a dataset analysis and conduct experimental downstream tasks. For financial sentiment/emotion classification tasks, DistilBERT outperforms other baselines, and for multivariate time series forecasting, a Temporal Attention LSTM model combining price index, text, and emotion features achieves the best performance than using a single feature.

show abstract

Improving exchange rate forecasting via a new deep multimodal fusion model

Cited by 15 publications

References 45 publications

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Ensemble learning technique to improve breast cancer classification model

StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series

Contact Info

Product

Resources

About