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

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

The parameters of biodiesel generation from Soybean oil having the molar ratio, reaction duration, and catalyst concentration for constant temperature, were modelled Using response surface methodology. a molar ratio of 6–12, NaOH of 1–2% w/w, time of 30–60 min, and temperature of 35–55 °C. an adaptive neuro-fuzzy inference system (ANFIS) and the response surface methodology-based Box–Behnken experimental design. a significant regression model with an R2 value of 0.9411 was obtained. The ANFIS model was used to link each of the four input factors with the outcome variable (biodiesel yield). In the training, an R2 value of 0.9918 was attained. The findings showed that the constructed models accurately depicted the processes they described. 

Performance evaluation of ANFIS and RSM in modeling biodiesel synthesis from soybean oil

Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis.

Analysis and Prediction of Healthcare Sector Stock Price Using Machine Learning Techniques: Healthcare Stock Analysis

The sense of which model to use for the prediction of time series data is very important. In this paper, we have done a Project Research on Linear Regression and Long short-term memory (LSTM) model both making a prediction using the Indian Stock Market Index Nifty 50 which is a time-series data. As we know the market changes daily and daily it touches new highs and lows and huge volume change is seen as well, so can linear regression help us predict a better future prediction or LSTM helps in predicting a better future prediction is what we have tested in this project. We have given our conclusion based on the result of the RMSE. The project in a way also shows the strength of linear regression and that of are different and shows a method of implementing time series data in both the models and checking their RMSE respectively.

Linear Regression vs LSTM for Time Series Data

There has been a lot of discussion about the rapid shift that is taking place in the global environment toward the adoption of online shopping on a massive scale as well as the previously unheard-of volatility of the global stock market. Both of these topics have received a lot of attention in recent times. Both of these subjects have generated a significant amount of debate recently. Making accurate predictions about the market has always been a cause of anxiety for investors. Investors have always been interested in finding a practical solution that could perfectly predict the price. This may be accomplished by having an awareness of how the market repeats itself or by analysing patterns that may assist achieve a higher return on investment. Investors have always been interested in discovering a solution that could accurately anticipate the price, regardless of the strategy that has been used. In this investigation, the methodologies of "Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Auto-Regressive Integrated Moving Average" are combined to create a one-of-a-kind hybrid model (ARIMA). This study analyses and predicts movements in the stock market by looking at a broad variety of technologies, including those that are presently in use. The overall objective of this research is to analyse and predict changes in the stock market. An investigation into the development of historical patterns was carried out with the assistance of the data collected from the companies whose stocks are traded on the National Stock Exchange (NSE). This investigation included a time span of six years and made use of the facts pertaining to the stock's previous close, open, high, and low values. We will utilise the historical trends that have occurred in the past so that we may analyse and forecast patterns that will occur in the days that are to come. This will be accomplished via the use of historical trends. Because of its capacity to process large datasets and do trend analysis, Python and R have attracted a lot of attention in recent years. 

Using ARIMA and LSTM to Implement Stock Market Analysis

It is complicated to estimate the stock market where relations between input and output are random in nature. Predicting the cost of the share market is the most complex job of the financial time series. Forecasting of the stock should be possible by utilizing the present and past information available on the market. The execution measurements that should be achieved if there should be an occurrence of the stock forecast are exactness, adaptability and less time utilization. There are numerous sorts of research done as such far with the end goal to predict the stock market to complete the characterized measurements. Several techniques have been accessible in data mining for forecasting the stock market, for example, Fuzzy systems, Artificial Neural Network (ANN), if-then-else rules, Bayesian algorithm et cetera. In this paper, the different strategies are available and used for forecasting the stock markets are talked about. This review knows which method is the finest to use for predicting the stock market. The most important application of market share is to predict market trends. Likewise, the market shows the performance of the future, which constantly encourages financial experts to understand when and what shares can be purchased to improve their risk. For this reason, a large number of research has been prepared so far to analyze the stock market by means of mining of the data.

A Survey on Stock Market Prediction

There has been increasing enthusiasm for displaying and determining stock costs over recent decades. The artificial neural network is not acceptable as it is a grouping of both fictitious and experimental disciplines, which can be a successful way to enhance the performance of the mix of different models if the model is very unusual. In this paper, the different strategies like linear regression, kNN, Naive, MA, AR, ARMA, ARIMA, and autoARIMA are used for forecasting the stock markets. This paper suggests which method is the best to use for predicting the stock market. This paper endeavors to address the determining of stock costs. On this unique circumstance, we gathered information on a month to month shutting stock indices of SENSEX. Subsequently, it very well may be utilized these models for estimating a task, particularly when higher anticipating precision is required.

Forecasting Price of Indian Stock Market Using Supervised Machine Learning Technique

Artificial intelligence (AI) seeks to replicate human psychological capacities. This is making an ideal change for healthcare services with the rapid availability of healthcare data and the rapid progress of analytics technology. AI tools are being used to diagnose major diseases such as cancer, neurology, liver diseases, cardiology, and so on. For the classification of the disease, several classification and dimensionality reduction algorithms are used. In this chapter, recent literature based on deep learning technologies has been reviewed to pursue healthcare domains and also various applications, and challenges of AI and deep learning that are used in this field have been discussed. Different AI algorithms (Linear SVM, SVM Grid Search, KNN, Logistic Regression, Decision Tree, Bagging, Boosting) have also been discussed in a nutshell. To predict diseases, all these algorithms will be implemented using various medical datasets, and also a comparison of all these algorithms will be shown.

AI-Driven Prognosis and Diagnosis for Personalized Healthcare Services: A Predictive Analytic Perspective

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A Survey on Stock Market Prediction

Forecasting Price of Indian Stock Market Using Supervised Machine Learning Technique

AI-Driven Prognosis and Diagnosis for Personalized Healthcare Services: A Predictive Analytic Perspective