Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models

Experts in the finance system have long found it difficult to estimate stock values. Despite the Efficient - market hypothesis Principle claim that it is difficult to anticipate share prices with any degree of precision, research has demonstrated that share price movements could be anticipated with the proper levels of precision provided the correct parameters are chosen and the proper predictive models are created. individuals who are adaptable. The share market is unpredictable in essence, making its forecasting a difficult undertaking. Stock prices are affected by more than economic reasons. In this project, Arima, LSTM and Prophet models are used to predict the future way of behaving share price, the datasets has been obtained from NSE, share price prediction algorithms have been created and tested. According to the empirical findings, the LSTM model would be used to anticipate share prices rather well over a substantial amount of time with exactness.

Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach

Burning fossil fuels results in emissions of carbon dioxide (CO2), which significantly contributes to atmospheric changes and climate disturbances. Consequently, people are becoming concerned about the state of the environment, and governments are required to produce precise projections to develop efficient preventive measures. This study makes a significant contribution to the area by modeling and predicting the CO2 emissions of vehicles using advanced artificial intelligence. The model was constructed using the CO2 emission by vehicles dataset from Kaggle, which includes different parameters, namely, vehicle class, engine size (L), cylinder transmission, fuel type, fuel consumption city (L/100 km), fuel consumption hwy (L/100 km), fuel consumption comb (L/100 km), fuel consumption comb (mpg), and CO2 emissions (g/km). To forecast the CO2 emissions produced by vehicles, a deep learning long short-term memory network (LSTM) model and a bidirectional LSTM (BiLSTM) model were developed. Both models are efficient. Throughout the course of the investigation, the researchers employed four statistical assessment metrics: the mean square error (MSE), the root MSE (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Based on the datasets of experiments carried out by Kaggle, the LSTM and BiLSTM models were created and implemented. The data were arbitrarily split into two phases: training, which included 80% of the total data, and testing, which comprised 20% of the total data. The BiLSTM model performed best in terms of accuracy and achieved high prediction values for MSE and RMSE. The BiLSTM model has the greatest attainable (R2 = 93.78). In addition, R% was used to locate a connection between the dataset’s characteristics to ascertain which characteristics had the highest level of association with CO2 emissions. An original strategy for the accurate forecasting of carbon emissions was developed as a result of this work. Consequently, policymakers may use this work as a potentially beneficial decision-support tool to create and execute successful environmental policies.

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Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model

An Empirical Analysis on the Portfolio of Transnational Auto Market Based on ARIMA-LSTM

Abstract Increasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA. 

/pdf/application-of-arima-lstm-for-manufacturing-decarbonization-1g53ndnv.pdf

Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts

The stock market is a highly volatile industry with ever changing bull (rise) and bear (fall) trends. This paper proposes a new hybrid model using Long ShortTerm Memory (LSTM), a Recurrent Neural Network (RNN) technique and Auto Regressive Integrated Moving Average (ARIMA), a time series forecasting technique to capture the live stock market data of S&P 500 using preexisting Application Programming Interface (API). Rise and fall in stock values in the previous years is analyzed. A novel LSTMARIMA hybrid is designed for capturing the linear and nonlinear portions of the time series. The Prophet forecasting library by Facebook has also been used that requires less preprocessing. Finally, both the approaches are compared and the one with better performance is accepted for the final stock market prediction system. In this case, Prophet has a high Root Mean Square Error (RMSE) of 27.59 and Mean Square Error (MSE) of 761.33 whereas the ARIMALSTM hybrid gives an MSE of 3.03 and RMSE of 1.74 along with a 99% fit of the model. Hence the hybrid performs much better than Prophet and is accepted as the final algorithm for implementation.

/pdf/an-arima-lstm-hybrid-model-for-stock-market-prediction-using-4skyug9vvm.pdf

Vijayalakshmi A

Papers

An ARIMA- LSTM Hybrid Model for Stock Market Prediction Using Live Data