Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction
The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI)
has become overwhelming, making it quite challenging for academics and relevant …
has become overwhelming, making it quite challenging for academics and relevant …
Bitcoin price forecasting with neuro-fuzzy techniques
Cryptocurrencies, with Bitcoin being the most notable example, have attracted considerable
attention in recent years, and they have experienced large fluctuations in their price. While a …
attention in recent years, and they have experienced large fluctuations in their price. While a …
[HTML][HTML] Designing fuzzy time series forecasting models: A survey
M Bose, K Mali - International Journal of Approximate Reasoning, 2019 - Elsevier
Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …
FQTSFM: A fuzzy-quantum time series forecasting model
P Singh - Information Sciences, 2021 - Elsevier
The study shows that there are two main problems that affect the performance of fuzzy time
series (FTS) models, namely the selection of the universe of discourse and the …
series (FTS) models, namely the selection of the universe of discourse and the …
Probabilistic forecasting with fuzzy time series
In recent years, the demand for develo** low computational cost methods to deal with
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …
[HTML][HTML] Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
Abstract Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a
well-known model for forecasting of seasonal time series that follow a long memory process …
well-known model for forecasting of seasonal time series that follow a long memory process …
[PDF][PDF] A tutorial on fuzzy time series forecasting models: Recent advances and challenges
Time series forecasting is a powerful tool in planning and decision making, from traditional
statistical models to soft computing and artificial intelligence approaches several methods …
statistical models to soft computing and artificial intelligence approaches several methods …
A new procedure in stock market forecasting based on fuzzy random auto-regression time series model
Various models used in stock market forecasting presented have been classified according
to the data preparation, forecasting methodology, performance evaluation, and performance …
to the data preparation, forecasting methodology, performance evaluation, and performance …
An interpretable Neural Fuzzy Hammerstein-Wiener network for stock price prediction
An interpretable regression model is proposed in this paper for stock price prediction.
Conventional offline neuro-fuzzy systems are only able to generate implications based on …
Conventional offline neuro-fuzzy systems are only able to generate implications based on …
To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles
Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency
market has witnessed the bursting of different bubbles in the last decade, which in turn have …
market has witnessed the bursting of different bubbles in the last decade, which in turn have …