A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

Unknown Aesopica

This chapter reviews the production of fables in Hans Blumenberg's “Unknown Aesopica: From Newly Found Fables” (1985). Aesop was — if he ever really existed — a slave. Based on his origin, he is referred to as a Phrygian, in some sources as Lydian, in better ones as Thracian. By betraying the secret of Aesop, Socrates subtly implied how the fable — which he was the first to put into verse while in prison awaiting his death — arrived at the inexhaustibility of its wisdoms: the forefather of all philosophy was also that of all fables. Thus, the disconcerting problem that Aesop placed himself on rare occasions in his stories (which were therefore suspected to be apocryphal) is finally solved. The new findings, three of which are presented here, prove that intermediate forms between animal fable and anecdote belong to the original stock: Aesop with his talking animals.

In Vivo Validation of the Viral Barcoding of Simian Immunodeficiency Virus SIVmac239 and the Development of New Barcoded SIV and Subtype B and C Simian-Human Immunodeficiency Viruses

ABSTRACT Genetically barcoded viral populations are powerful tools for evaluating the overall viral population structure as well as assessing the dynamics and evolution of individual lineages in vivo over time. Barcoded viruses are generated by inserting a small, genetically unique tag into the viral genome, which is retained in progeny virus. We recently reported barcoding the well-characterized molecular clone simian immunodeficiency virus (SIV) SIVmac239, resulting in a synthetic swarm (SIVmac239M) containing approximately 10,000 distinct viral clonotypes for which all genetic differences were within a 34-base barcode that could be tracked using next-generation deep sequencing. Here, we assessed the population size, distribution, and authenticity of individual viral clonotypes within this synthetic swarm using samples from 120 rhesus macaques infected intravenously. The number of replicating barcodes in plasma correlated with the infectious inoculum dose, and the primary viral growth rate was similar in all infected animals regardless of the inoculum size. Overall, 97% of detectable clonotypes in the viral stock were identified in the plasma of at least one infected animal. Additionally, we prepared a second-generation barcoded SIVmac239 stock (SIVmac239M2) with over 16 times the number of barcoded variants of the original stock and an additional barcoded stock with suboptimal nucleotides corrected (SIVmac239Opt5M). We also generated four barcoded stocks from subtype B and C simian-human immunodeficiency virus (SHIV) clones. These new SHIV clones may be particularly valuable models to evaluate Env-targeting approaches to study viral transmission or viral reservoir clearance. Overall, this work further establishes the reliability of the barcoded virus approach and highlights the feasibility of adapting this technique to other viral clones. IMPORTANCE We recently developed and published a description of a barcoded simian immunodeficiency virus that has a short random sequence inserted directly into the viral genome. This allows for the tracking of individual viral lineages with high fidelity and ultradeep sensitivity. This virus was used to infect 120 rhesus macaques, and we report here the analysis of the barcodes of these animals during primary infection. We found that the vast majority of barcodes were functional in vivo. We then expanded the barcoding approach in a second-generation SIVmac239 stock (SIVmac239M2) with over 16 times the number of barcoded variants of the original stock and a barcoded stock of SIVmac239Opt5M whose sequence had 5 changes from the wild-type SIVmac239 sequence. We also generated 4 barcoded stocks from subtype B and C SHIV clones each containing a human immunodeficiency virus (HIV) type 1 envelope. These virus models are functional and can be useful for studying viral transmission and HIV cure/reservoir research.

Preparation, standardization and in vitro safety testing of Mycobacterium nosodes (Emtact- polyvalent nosode)

Background: Most of the nosodes in the homeopathic pharmacopeia have been sourced from obscure pathological material over a century ago; of which no scientific documentation is available. Method: A method for preparation and standardization of univalent and polyvalent Mycobacterium nosodes (labeled as Emtact), using different strains of Mycobacterium tuberculosis was developed. The committee comprising microbiologists, scientist, pharmacist, homeopaths and clinicians had reviewed and approved the method of preparation of nosode. Preparation of the nosode was based on the reference in the Homeopathy Pharmacopoeia of India (HPI), group N-IV. Strains of M. tuberculosis viz. Standard strain H37Rv, multi-drug resistant (MDR) M. tuberculosis, Mycobacterium bovis (BCG vaccine) and Mycobacterium avium were identified, procured and documented. Twenty billion viable cells for each strain were taken for Original Stock Nosode (OSN). The original stock was prepared by suspending the microbial cells into water for injection (WFI) (1 ml). As per the Indian Pharmacopoeia (IP) monograph, sterility testing was done for different potencies. Polymerase Chain Reaction (PCR) was performed for 30c potency for detection of any DNA material of the source organisms. Result: A polyvalent (multi-strain) and univalent M. tuberculosis nosodes were prepared for research and clinical use. No growth of Mycobacterium was observed in any of the samples above 5c potency. The in-vitro testing for nosode (30c) was found to be free from any organism and DNA material. Conclusion: Mycobacterium nosodes sourced from individual strain and polyvalent Emtact nosode in vitro testing results found to be satisfactory for its handling and utilization. The nosode seems to be safe and may be tested further in vivo to explore its therapeutic application.

HYBRID DECOMPOSITION AND ENSEMBLE FRAMEWORK FOR STOCK PRICE FORECASTING: A COMPARATIVE STUDY

In this study, a hybrid decomposition and ensemble framework incorporating Ensemble empirical mode decomposition (EEMD) and selected modeling methodologies are proposed for stock price forecasting. Under the framework, the original stock price series was first decomposed into several subseries including a number of intrinsic mode functions (IMFs) and a residue using EEMD technique. Then, extracted subseries was modeled to generate forecasts respectively. Finally, the forecasts of all extracted subseries were aggregated to produce an ensemble forecasts for the original stock price series. An extensive experiment was conducted to compare the feasibility and validity of the proposed hybrid framework employing different modeling methodologies, such as support vector machines (SVMs) (in the formulation of support vector regression (SVR), feed forward neural networks (FNN), and autoregressive integrated moving average (ARIMA). The real daily closing price series of Thirty Dow Jones industrial stocks from New York Stock Exchange (NYSE) was used for experimental evaluation. The results demonstrate that significant improvement can be achieved with the proposed hybrid decomposition and ensemble framework across all the three modeling methodologies, particularly, hybrid EEMD-based FNN modeling framework achieved the most significant improvement but hybrid EEMD-based SVMs modeling framework performed best in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and directional symmetry (DS).