Short-Timescale Gravitational Microlensing Events Prediction with ARIMA-LSTM and ARIMA-GRU Hybrid Model

Sun, Ying; Zhao, Zijun; Ma, Xiaobin; Du, Z.

doi:10.1007/978-3-030-28061-1_23

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Both statistical models and neural network models have their own advantages and limitations. Therefore, some scholars also constructed a combination model for financial time series made predictions and confirmed the advantages of the combined model over the single model [5].…”

Section: Assets Price Predictionmentioning

confidence: 77%

A Two-stage Model: Investment Market Trading Model Based on Prediction and Adaptive Strategies

Li¹,

Hu²,

Wu³

2022

BCPBM

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Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. Two such assets are gold and bitcoin. This paper constructs a two-stage model for price prediction and trading strategy formulation. Firstly, we build a dynamic ARIMA-LSTM hybrid model. And before applying it, we use CEEMDAN method to decompose the non-stationary time series first and then reconstruct the final result by predicting each IMF and summing up weighted. And then the model can update the training set dynamically when new price data is released. After getting prices predicted, we calculated several quantitative trading indicators so that we can make decisions more comprehensively instead of only focusing on the predicted price. And we use semi-supervised SVM to develop an adaptive strategy to maximize the total return. Finally, we demonstrate the superiority of our strategy from two perspectives. In actual investment transactions, the two-stage model can be used as a guide for the formulation of trading strategies, thereby avoiding risks and increasing returns.

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Section: Assets Price Predictionmentioning

confidence: 77%

A Two-stage Model: Investment Market Trading Model Based on Prediction and Adaptive Strategies

Li¹,

Hu²,

Wu³

2022

BCPBM

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“…It can be concluded that the replenishment strategy is that the replenishment quantity equals the predicted sales volume. Take the ARIMA model [4] and take advantage of its underlying time patterns by fitting historical data. The ARIMA model can provide a prediction of future sales values.…”

Section: Quantity Of Salementioning

confidence: 99%

Research on Vegetables Sales Profit Based on Machine Learning

Liu,

Li,

2024

HSET

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The sales volume of different vegetables in supermarkets is different, and the profit is different. The research on the profit of each category of vegetables can help supermarkets predict the sales revenue more accurately to improve inventory management, purchasing plans, and sales strategy. This can help the supermarket avoid excess or insufficient inventory, improve profits, and help the supermarket identify potential sales risks. The research team obtained daily sales data for various categories of vegetables from a supermarket on the Internet for some time. First, according to the "cost plus pricing" method of vegetables, regression analysis was conducted on the total cost and total profit of various categories of vegetables, and it was found that there was a strong linear relationship, and the profit rate of various categories of vegetables was relatively fixed. They were [0.6629, 0.5915, 0.4663, 0.5121, 0.5596, 0.5381]. Since the sales volume of vegetables has seasonal characteristics, this paper established the ARIMA model to forecast the sales volume of each category of vegetables; and obtained the sales volume distribution of each category of vegetables from July 1 to 7, 2023. The BP neural network model with 22 layers of hidden layers was established, and the purchase unit price data could be obtained according to the known sales data. The R square of the model was greater than 0.7, and the goodness of fit was high. According to the known correlation, considering the impact of the loss rate on the profit and the rationality of the data, a multivariate linear programming model is established to give the replenishment volume and pricing strategy from July 1 to 7, 2023. It is concluded that the maximum profit of the supermarket in these seven days is [845.1835, 870.5028,861.6210, 858.8551, 839.4595, 864.7580, 892.9897]. Through the sensitivity analysis of the profit rate of each category of vegetables, it is concluded that the profit rate increases by 1%, the income will increase by about 1.1%, and the model is robust.

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“…Intelligent models represented by artificial neural network (ANN) are good at dealing with incomplete, fuzzy, uncertain or irregular data and have a good fit for nonlinear relations [3] . Zhang (2003) [4] once discussed a combination model of ARIMA and neural network, and based on this idea, people built a combination model for prediction, and confirmed that the combination model is more advantageous [5] .…”

Section: Introductionmentioning

confidence: 97%

Carbon Trading Price Forecast Based on LSTM and ARIMA—Take the Shanghai Area for Example

2022

AJBM

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In order to solve environmental problems such as air pollution, China has tried to carry out carbon emission trading market. For the sake of improving the risk management ability of the carbon emission trading market and make reasonable policies, it is very important to analyze and forecast the price of the carbon emission trading market. Considering that a single model has many self-limitations, in order to ensure that the predicted results can better match the actual situation, this paper adopts the combination model of ARIMA and LSTM to study the carbon emission trading price. Based on the reasonable prediction of carbon emission trading price of Shanghai Stock Exchange in recent years, the results show that ARIMA and LSTM can well combine their mutual advantages, effectively improve the accuracy of prediction, and provide certain guarantee for the sound development of carbon emission trading market.

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Short-Timescale Gravitational Microlensing Events Prediction with ARIMA-LSTM and ARIMA-GRU Hybrid Model

Cited by 5 publications

References 10 publications

A Two-stage Model: Investment Market Trading Model Based on Prediction and Adaptive Strategies

A Two-stage Model: Investment Market Trading Model Based on Prediction and Adaptive Strategies

Research on Vegetables Sales Profit Based on Machine Learning

Carbon Trading Price Forecast Based on LSTM and ARIMA—Take the Shanghai Area for Example

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