Research on Precoding Performance Optimization of MU-MIMO System Based on WPCN Slot Allocation

Li, Zhengrong; Li, Cheng; Wei, Lei; Zou, Lianying

doi:10.1109/icacr53472.2021.9605197

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The methods for stock price forecasting generally include fundamental, technical analysis, statistical methods, and neural network forecasting methods, with the latter two being studied in greater greater details in the last two years. Li Zhengrong used a combination of weighted support vector machine and relief algorithm to predict the trend of stock data in six industries with an accuracy of more than 70% [11]; Gupta improved Markov Model and compared the improved model with ARIMA, and ANN (artificial neuron network), which paved a new way for stock price forecasting [12]. In terms of neural network prediction, Zhang Ni used an LSTM (Long Short-term Memory) model to predict the stock price of Guizhou Maotai with a good fit [13].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Liquor Index: Applications of ARIMA and Gray Model

Tan¹,

Gao²,

Gao³

2023

Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022

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An imperfect stock market provides ambitious investors with plenty of room for arbitrage. Currently, ARIMA (Autoregressive Integrated Moving Average) and Gray forecast models are widely used to forecast future stock prices. In our paper, we aim to investigate the efficiency of those two models. Since each stock is idiosyncratic by nature, it is not advisable to look for one single forecast method that is robust for forecasting all the stocks in the market. Thus, we select the Chinese liquor industry, one of the most popular industry to invest in for the past year in China, as the context to study these two models. To compare the efficiency of those two models for forecasting the Chinese liquor index specifically, we first format the data into the appropriate form and then test various assumptions hidden in ARIMA and Gray forecasting models. After applying ARIMA and Gray forecast models respectively to predict stock closing prices for 20 days, we conclude that the ARIMA model performs better with a smaller sum of squared residuals. As a result, we deduce that ARIMA has a better ability of forecasting Liquor Index than Gray model. Methods incorporating more factors to forecast index deserve further investigation.

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Section: Introductionmentioning

confidence: 99%