摘要
Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities.The network will be trained and evaluated for accuracy with various sizes of data sets,i.e.weekly historical data of MCX,GOLD,COPPER and the results will be calculated.Design/methodology/approach-Desirable LSTM model for script price forecasting from the perspective of minimizing MSE.The approach which we have followed is shown below.(1)Acquire the Dataset.(2)Define your training and testing columns in the dataset.(3)Transform the input value using scalar.(4)Define the custom loss function.(5)Build and Compile the model.(6)Visualise the improvements in results.Findings-Financial series is one of the very aged techniques where a commerce person would commerce financial scripts,make business and earn some wealth from these companies that vend a part of their business on trading manifesto.Forecasting financial script prices is complex tasks that consider extensive human-computer interaction.Due to the correlated nature of financial series prices,conventional batch processing methods like an artificial neural network,convolutional neural network,cannot be utilised efficiently for financial market analysis.We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic(LSTM).The LSTM Classic is quite different from normal LSTM as it has customised loss function in it.This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure,and it helps forecast financial time series.Financial Series Index is the combination of various commodi
