A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting

Zhang, Junting; Liu, Haifei; Bai, Wei; Li, Xiaojing

doi:10.1016/j.najef.2023.102022

Cited by 19 publications

(4 citation statements)

References 45 publications

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“…The combined forecasting model also reflects higher accuracy in forecasting other time series. Regarding stock prices, indices, and futures prices, the combined forecasting model established using ARIMA and LSTM also obtained better forecasting results [49]. In terms of the prediction accuracy of the dynamic gas emission concentration in the coal mining face, wavelet decomposition and the GM-ARIMA-based prediction method are proposed to improve the fitting effect and attain a higher prediction accuracy when compared with results obtained by the GM (1.1), ARIMA, and their combination, called the GM-ARIMA prediction model [50].…”

Section: Combination Of Modelsmentioning

confidence: 96%

Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice

Dai,

Gao,

2024

Buildings

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As large-scale infrastructure construction projects conclude, the overall civil construction market shrinks, leading to increased competition among construction companies. Accordingly, various construction companies are gradually emphasizing the issue of project costs. Numerous studies have shown the impact of material costs on the overall project cost. However, sharp fluctuations in material prices have been observed in recent years due to various unstable factors in the market. Thus, accurate prediction of material prices facilitates the development of appropriate material procurement strategies to deal with market risks. This paper collects the rebar prices announced in the first half of 2023 in China’s Guangdong Province, selects one type of rebar price as a representative, and analyzes the time series characteristics of the rebar price composition. Then, it judges whether the time series passes the white noise detection, grey correlation detection, and level ratio detection. The prediction model is established based on the seasonal auto-regressive integrated moving average (SARIMA) model and the grey model (GM) (1.1) to predict future rebar prices. According to the characteristics of the rebar price data in June 2023, the residual inverse method combines the results predicted by the two models. The price in the early and middle of July 2023 is then predicted using the newly constructed combined model. The results indicate that the combined model is more accurate than the single prediction model.

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Section: Combination Of Modelsmentioning

confidence: 96%

Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice

Dai,

Gao,

2024

Buildings

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“…ARIMA (autoregressive moving average model) is a time series analysis technology, which is used to model and predict regular time series data [1][2][3][4]. It is based on autoregressive (AR), moving average (MA) and mixed (ARMA) definitions.…”

Section: Establishment Of Time Series Modelmentioning

confidence: 99%

Research on Wordle Based on Time Series Model and Grey Prediction Model

2023

AJCIS

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In this paper, the time series model and grey prediction model are established, and the stability of ARIMA model is tested by difference test, and the prediction is carried out. The prediction result is taken as the upper interval of the prediction interval, and the grey prediction result is taken as the lower interval of the prediction interval. The time series and grey prediction models are established respectively. Secondly, this paper establishes Pearson correlation coefficient model to analyze the relationship between lexical attributes and the percentage of difficulty pattern attempt scores. Three indicators are selected for the attributes of words: word complexity, letter frequency and the number of repeated letters, which are analyzed by taking the average number of attempts. It is found that these three indicators are significantly correlated with the number of attempts, the number of letter repetition and word complexity are positively correlated with the average number of attempts, and the letter frequency is negatively correlated with the average number of attempts.

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“…These three stages are described in detail in the following paragraphs. Statistical learning-based forecasting models primarily contain the moving average (MA) model (Abellana, 2021), the Autoregressive Integrated Moving Average (ARIMA) model (Su, C., 2020), the variant ARIMA model (Zhang, J., et al, 2024), the Seasonal Autoregressive Integrated Moving Average (SARIMA) model , and the Kalman filter model, etc. Although these models are theoretically clear and simple to implement, they have poor prediction accuracy when dealing with large-scale complex data tasks.…”

Section: Related Workmentioning

confidence: 99%

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

Liu

2023

International Journal of Swarm Intelligence Research

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To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.

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A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting

Cited by 19 publications

References 45 publications

Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice

Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice

Research on Wordle Based on Time Series Model and Grey Prediction Model

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

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