[HTML][HTML] An overview: Modeling and forecasting of time series data using different techniques in reference to human stress
SG Wawale, A Bisht, S Vyas, C Narawish… - Neuroscience Informatics, 2022 - Elsevier
Forex is an important currency indicator. The index is a major factor in the development of
the country. This look examines the effects of currency trading on the Random stroll version …
the country. This look examines the effects of currency trading on the Random stroll version …
Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market
Technical analysis indicators are popular tools in financial markets. These tools help
investors to identify buy and sell signals with relatively large errors. The main goal of this …
investors to identify buy and sell signals with relatively large errors. The main goal of this …
Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: contextual evidence from India using time series forecasting tools
Stock market price prediction is considered a critically important issue for designing future
investments and consumption plans. Besides, given the fact that the COVID-19 pandemic …
investments and consumption plans. Besides, given the fact that the COVID-19 pandemic …
A bibliometric literature review of stock price forecasting: from statistical model to deep learning approach
PH Vuong, LH Phu, TH Van Nguyen… - Science …, 2024 - journals.sagepub.com
We introduce a comprehensive analysis of several approaches used in stock price
forecasting, including statistical, machine learning, and deep learning models. The …
forecasting, including statistical, machine learning, and deep learning models. The …
Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information
G Song, T Zhao, S Wang, H Wang, X Li - Information Sciences, 2023 - Elsevier
The volatility of stock prices makes it difficult to predict stock price trends correctly. This
volatility is affected by many factors, including other stocks related to it. Stock prediction …
volatility is affected by many factors, including other stocks related to it. Stock prediction …
Stock market forecasting using the random forest and deep neural network models before and during the COVID-19 period
Stock market forecasting is considered the most challenging problem to solve for analysts. In
the past 2 years, Covid-19 has severely affected stock markets globally, which, in turn …
the past 2 years, Covid-19 has severely affected stock markets globally, which, in turn …
Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19
This paper investigates volatility spillovers in the stock market in Japan during the COVID-19
pandemic by using GARCH family models. The empirical analysis is focused on the …
pandemic by using GARCH family models. The empirical analysis is focused on the …
[HTML][HTML] Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh
During the third wave of the coronavirus epidemic in Bangladesh, the death and infection
rate due to this devastating virus has increased dramatically. The rapid spread of the virus is …
rate due to this devastating virus has increased dramatically. The rapid spread of the virus is …
[PDF][PDF] Estimating fluctuating volatility time series returns for a cluster of international stock markets: A case study for Switzerland
The major aim of this empirical study is to estimate the volatility time series returns for a
cluster of international stock markets, such as: Switzerland, Austria, China and Hong Kong …
cluster of international stock markets, such as: Switzerland, Austria, China and Hong Kong …
Time Series analysis with ARIMA for historical stock data and future projections
Forecasting stock prices is difficult because of the many unknowns and diverse factors that
affect the financial market. Using time series data, the study attempts to assess how well the …
affect the financial market. Using time series data, the study attempts to assess how well the …