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 …
[HTML][HTML] Hybrid wavelet-neural network models for time series
The use of wavelet analysis contributes to better modeling for financial time series in the
sense of both frequency and time. In this study, S&P500 and NASDAQ data are separated …
sense of both frequency and time. In this study, S&P500 and NASDAQ data are separated …
Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models
J Han, H Lin, Z Qin - Applied Sciences, 2023 - mdpi.com
An increase in the carbon dioxide (CO2) concentration within a vehicle can lead to a
decrease in air quality, resulting in numerous adverse effects on the human body. Therefore …
decrease in air quality, resulting in numerous adverse effects on the human body. Therefore …
Forecasting trends in food security with real time data
Early warning systems are an essential tool for effective humanitarian action. Advance
warnings on impending disasters facilitate timely and targeted response which help save …
warnings on impending disasters facilitate timely and targeted response which help save …
Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM
Background Missing observations within the univariate time series are common in real-life
and cause analytical problems in the flow of the analysis. Imputation of missing values is an …
and cause analytical problems in the flow of the analysis. Imputation of missing values is an …
Optimizing electric vehicle charging station location on highways: A decision model for meeting intercity travel demand
Electric vehicles have emerged as one of the top environmentally friendly alternatives to
traditional internal combustion engine vehicles. The development of a comprehensive …
traditional internal combustion engine vehicles. The development of a comprehensive …
[HTML][HTML] Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions
The use of artificial intelligence in renewable energy systems increases energy generation
and improves energy system management. The control system of many solar trackers is …
and improves energy system management. The control system of many solar trackers is …
Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System with Machine Learning and Statistical Models
We benchmark performance of long-short term memory (LSTM) network ML model and
Autoregressive Integrated Moving Average (ARIMA) statistical model in temporal forecasting …
Autoregressive Integrated Moving Average (ARIMA) statistical model in temporal forecasting …
Investment decision on cryptocurrency: comparing prediction performance using ARIMA and LSTM
The increasing popularity of cryptocurrencies as a means of financial inclusion for
investment and trade has become a major concern for individuals seeking to benefit from the …
investment and trade has become a major concern for individuals seeking to benefit from the …
Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings
This paper aims to provide discernment toward establishing a general framework, dedicated
to data analysis and forecasting in smart buildings. It constitutes an industrial return of …
to data analysis and forecasting in smart buildings. It constitutes an industrial return of …