Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends

Theerthagiri, Prasannavenkatesan; Ruby, A. Usha

doi:10.1007/s11042-023-14819-x

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…Smell Index = sort(S) (17) where DF denotes the current population's best fitness value; S(i) denotes the fitness value of an individual slime mold; N denotes the total number of slime molds in the population; condition represents the top 1/2 ranking individuals in the slime mold population based on their S(i) values. The remaining individuals in the population are denoted by others; t max denotes the maximum number of iterations; b F denotes the best fitness value attained by an individual during the current iteration process; W F denotes the best fitness value attained by an individual during the current iteration process; Sort means to arrange the population's fitness values in ascending order; SmellIndex denotes the sequence after arranging fitness values in order.…”

Section: The Sma Optimization Algorithmmentioning

confidence: 99%

“…Wang et al utilized the comprehensive degradation index (CDI) in the time-frequency domain and long shortterm memory (LSTM) to construct a trend prediction model for the state of hydropower units, achieving the prediction of the degradation trend of hydropower units and improving the prediction accuracy [16]. Theerthagiri et al utilized the Seasonal ARIMA (SARIMA) model combined with the weighted average method and feedback error analysis method to forecast crude oil prices, successfully improving the prediction accuracy and obtaining a more accurate trend of crude oil price changes [17]. Xu et al developed a greenhouse microclimate trend prediction model based on an improved empirical mode decomposition (IEMD)-optimized informer.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

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Section: The Sma Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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“…The SARIMA model has been applied to the price forecasting of crude oil, agricultural products, and electricity, attaining excellent results [36][37][38][39][40][41][42]. Based on its characteristics, the SARIMA model is combined with other models to establish a new prediction model [43][44][45].…”

Section: Sarimamentioning

confidence: 99%

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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“…This approach assumes that stock prices exhibit recurrent patterns and behaviors over time, making it possible to identify and extrapolate trends for predictive purposes. Time series models encompass a range of techniques, from basic moving averages to advanced autoregressive integrated moving average (ARIMA) models, all designed to capture and exploit temporal dependencies in the data [5].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction based on Time Series Model and Long Short-term Memory Method

Song,

Song

2024

HBEM

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This study conducts a comparative analysis of two prominent methodologies, Time Series Analysis and Long Short-Term Memory Neural Networks (LSTM), for the prediction of stock prices, utilizing historical data from Netflix. The primary purpose of conducting this research is to evaluate their efficacy in terms of predictive accuracy. Time Series Analysis encompasses stationarity tests, rolling statistics, and the application of the Autoregressive Integrated Moving Average model. In contrast, LSTM Neural Networks involve data normalization, reshaping, and the development of LSTM-based models. Performance assessment metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and visual comparisons are utilized. The results prominently favor long short-term memory Neural Network, which consistently outperforms in predictive accuracy, yielding reduced forecasting errors. This study contributes significant insights into stock price prediction methodologies and offers implications for refining model parameters, bolstering adaptability to evolving market dynamics, and addressing computational efficiency concerns in both Time Series Analysis and LSTM Neural Networks. In summary, LSTM model emerges as the preferred approach, advancing understanding of effective strategies for stock price prediction in financial markets.

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Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends

Cited by 13 publications

References 38 publications

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

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

Stock Price Prediction based on Time Series Model and Long Short-term Memory Method

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