Accurately predicting the prices of financial time series is essential and challenging for the
financial sector. Owing to recent advancements in deep learning techniques, deep learning …

Time series forecasting is a widely applied approach in sequential data series including the
stock market. Time series forecasting can be examined through single step ahead as well as …

Fusion is a state-of-the-art technique to observe the behavioral pattern from time series data.
Fusion models efficiently and effectively interpret both linear and nonlinear patterns that are …

Accurate financial time series forecasting is important in financial markets. However, for
financial time series with low fluctuation, there is an unusual forecasting phenomenon in the …

Financial engineering is crucial for effectively combining finance with quantitative
approaches. This study aims to forecast the performance of the Nasdaq stock market by …

Accurate and efficient predictions concerning stock prices are an intriguing and sought-after
task in the field of computational financial analysis. This paper aims to leverage and validate …

Multi-step stock index forecasting is vital in finance for informed decision-making. Current
forecasting methods for this task frequently produce unsatisfactory results due to the …

Predicting volatility in financial markets is an important task with practical uses in decision-
making, regulation, and academic research. This study focuses on forecasting realized …

Time series data is data that is collected periodically and has certain time intervals. Time
series data is widely available in the fields of finance, meteorology, signal processing …