Autoregressive models in environmental forecasting time series: a theoretical and application review
Though globalization, industrialization, and urbanization have escalated the economic
growth of nations, these activities have played foul on the environment. Better understanding …
growth of nations, these activities have played foul on the environment. Better understanding …
Stock price prediction using CNN-BiLSTM-Attention model
J Zhang, L Ye, Y Lai - Mathematics, 2023 - mdpi.com
Accurate stock price prediction has an important role in stock investment. Because stock
price data are characterized by high frequency, nonlinearity, and long memory, predicting …
price data are characterized by high frequency, nonlinearity, and long memory, predicting …
Stock price prediction based on LSTM and LightGBM hybrid model
L Tian, L Feng, L Yang, Y Guo - The Journal of Supercomputing, 2022 - Springer
Finding an accurate, stable and effective model to predict the rise and fall of stocks has
become a task increasingly favored by scholars. This paper proposes a long short-term …
become a task increasingly favored by scholars. This paper proposes a long short-term …
Novel stock crisis prediction technique—a study on indian stock market
N Naik, BR Mohan - IEEE Access, 2021 - ieeexplore.ieee.org
A stock market crash is a drop in stock prices more than 10% across the major indices. Stock
crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell …
crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell …
Echo state network and classical statistical techniques for time series forecasting: A review
Forecasting is an extensive field of study, which tries to avoid injuries, diseases, and
damages but also can help in energy production, finance investments, etc. Two mathematics …
damages but also can help in energy production, finance investments, etc. Two mathematics …
Long time series deep forecasting with multiscale feature extraction and Seq2seq attention mechanism
Long time series forecasting is an important problem with applications in many fields, such
as weather forecasting, stock prediction, petroleum production prediction and heating load …
as weather forecasting, stock prediction, petroleum production prediction and heating load …
Water quality prediction of the yamuna river in India using hybrid neuro-fuzzy models
The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle
swarm optimization, genetic algorithm, harmony search, and teaching–learning-based …
swarm optimization, genetic algorithm, harmony search, and teaching–learning-based …
Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm
Stock index price forecasting is the influential indicator for investors and financial
investigators by which decision making capability to achieve maximum benefit with minimum …
investigators by which decision making capability to achieve maximum benefit with minimum …
A novel hybrid model for stock price forecasting integrating encoder forest and informer
S Ren, X Wang, X Zhou, Y Zhou - Expert Systems with Applications, 2023 - Elsevier
Stock forecasting plays a pivotal role in time series forecasting as it enables informed and
effective investment decisions by minimizing risks. In this paper, a novel hybrid model for …
effective investment decisions by minimizing risks. In this paper, a novel hybrid model for …
Predicting the highest and lowest stock price indices: A combined BiLSTM-SAM-TCN deep learning model based on re-decomposition
H Gong, H **ng - Applied Soft Computing, 2024 - Elsevier
Accurate prediction of stock price indices is crucial for market participants to obtain valuable
information and mitigate risks. For more accurate forecasting of stock price indices, this study …
information and mitigate risks. For more accurate forecasting of stock price indices, this study …