Suchergebnisse

The stock price reflects various factors such as the rate of economic growth, inflation, overall economy, trade balance, and monetary system, all of which impact the stock market as a whole. Investors often find the principle of stock price trends unclear because of the many important variables involved. When creating an investment strategy or determining the timing for buying or selling stocks, forecasting stock market trends plays a critical role. It is difficult to estimate the value of the stock market due to the non-linear and dynamic nature of the stock index. Numerous studies using deep learning techniques have been successful in making such predictions. The Long Short Term Memory (LSTM) has become popular for predicting stock market prices. This paper thoroughly examines methods for predicting stock market performance using RNN-LSTM and GA-LSTM, provides explanations of these methods, and performs a comparative analysis. We will discuss future directions and outline the significance of using RNN-LSTM and GA-LSTM for forecasting stock market trends, based on the papers we have reviewed.

In the era of big data, accurate forecasting of corporate data is crucial for formulating effective strategies and decisions. This paper focuses on the prediction of key corporate indicators, taking TSLA, JD, MSFT, and TCEHY as case studies. It explores the application of three forecasting models: ARIMA-GARCH, LSTM, and Wavelet-LSTM. By comparing the predictive accuracy of these models, we find that each model has its strengths and weaknesses under different data characteristics. The study not only emphasizes the importance of accurate forecasting for corporate management and market prediction but also summarizes the adaptability and limitations of different models in dealing with complex time series data, providing valuable reference and insights for similar forecasting tasks.

AbstractWe consider a simple long short-term memory (LSTM) neural network extension of the Poisson Lee-Carter model, with a particular focus on different procedures for how to use training data efficiently, combined with ensembling to stabilise the predictive performance. We compare the standard approach of withholding the last fraction of observations for validation, with two other approaches: sampling a fraction of observations randomly in time; and splitting the population into two parts by sampling individual life histories. We provide empirical and theoretical support for using these alternative approaches. Furthermore, to improve the stability of long-term predictions, we consider boosted versions of the Poisson Lee-Carter LSTM. In the numerical illustrations it is seen that even in situations where mortality rates are essentially log-linear as a function of calendar time, the boosted model does not perform significantly worse than a simple random walk with drift, and when non-linearities are present the predictive performance is improved. Moreover, boosting allows us to obtain reasonable model calibrations based on as few data points as 20 years.

Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government&rsquo ; s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile &ldquo ; impression ecology&rdquo ; platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM&rsquo ; s performance in prediction performance and level decision-making effectiveness and robustness.