Stock Price Prediction Using Time Series

Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant

Trajectory Track for the Landing of Carrier Aircraft with the Forecast on the Aircraft Carrier Deck Motion

The paper presents a prediction method of deck lateral-directional motion for the control of landing trajectory of aircraft. Firstly, through the analysis of the process of aircraft returning to the ship, the modeling of the motion has been built. Secondly, in view of the delay of trajectory tracking captured in the actual process of aircraft landing on the ship, the error caused by the carrier motion signal has been analyzed. Based on the simulation results, the recommended prediction time of carrier motion has been proposed.

Combined detection of supernova neutrino signals

Supernova neutrino detection in neutrino and dark matter experiments is usually implemented as a real-time trigger system based on counting neutrino interactions within a moving time window. The sensitivity reach of such experiments can be improved by taking into account the time profile of the expected signal. We propose a shape analysis of the incoming experimental data based on a log likelihood ratio variable containing the assumed signal shape. This approach also allows a combination of potential supernova signals in different detectors for a further sensitivity boost. The method is tested on the NOvA detectors to study their combined sensitivity to the core-collapse supernova signal, and also on KamLAND, Borexino and SK-Gd as potential detectors of presupernova neutrinos. Using the shape analysis enhances the signal significance for supernova detection and prediction, as well as the sensitivity reach of the experiment. It also extends the supernova prediction time when applied to the presupernova neutrino signal detection. Enhancements achieved with the shape analysis persist even in the case when the actual signal doesn't match the expected signal model.

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

Edge Computing Using Embedded Webserver with Mobile Device for Diagnosis and Prediction of Metastasis in Histopathological Images

Diagnosis of different breast cancer stages using histopathology whole slide images is the gold standard in grading the tissue metastasis. Traditional diagnosis involves labor intensive procedures and is prone to human errors. Computer aided diagnosis assists medical experts as a second opinion tool in early detection which prevents further proliferation. Computing facilities have emerged to an extent where algorithms can attain near human accuracy in prediction of diseases, offering better treatment to curb further proliferation. The work introduced in the paper provides an interface in mobile platform, which enables the user to input histopathology image and obtain the prediction results with its class probability through embedded web-server. The trained deep convolutional neural networks model is deployed into a microcomputer-based embedded system after hyper-parameter tuning, offering congruent performance. The implementation results show that the embedded platform with custom-trained CNN model is suitable for medical image classification, as it takes less execution time and mean prediction time. It is also noticed that customized CNN classifier model outperforms pre-trained models when used in embedded platforms for prediction and classification of histopathology images. This work also emphasizes the relevance of portable and flexible embedded device in real time clinical applications.

The Medium-to-Short-Term Earthquake Predictions in China and Their Evaluations Based on the R-Score

The earthquake tendency consultations in China, which have been carried out by the China Earthquake Administration for more than 40 yr, are really forward prediction of earthquakes. The results, experiences, and data accumulation are valuable for seismic researches. In this article, the annual, monthly, and weekly predictions produced by the regular earthquake tendency consultations and the rapid postearthquake tendency prediction derived from the irregular ones are presented systematically. In the regular predictions, the areas where earthquakes tend to occur are identified by specific space–time windows. To evaluate the efficiency of the predictions, we apply the R-score method to all the medium-to-short-term efforts. The R-score has been used as a routine tool to test annual predictions in China, in which the hit rate and the percentage of spatial alarms over the whole territory are taken into consideration. Results show that the annual R-scores, during the period of 1990–2020, increased gradually, with the average of 0.293. The examples in 2018 indicate that a considerable proportion of earthquakes with the Ms 5.0 and above were detected by the annual prediction; some earthquakes were detected by the monthly prediction, whereas just only a few earthquakes could be detected by the weekly prediction. The corresponding R-scores are 0.46, 0.11, and 0.002, decreasing obviously with reduction of the prediction time windows, and the smallest one, which is very close to zero, may suggest the minimum time scale for an effective earthquake prediction. We also evaluated efficiency of the irregular predictions by analyzing the practices of 29 Ms≥5.0 earthquakes since January 2019 and found that it is highly possible to do rapid postearthquake tendency prediction in China.

Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function

In order to solve the problems of low accuracy and long prediction time of traditional economic growth prediction algorithms in coastal areas, an algorithm based on impulse response function was designed to analyze economic growth prediction in coastal areas. Crawler technology is used to capture the economic data of coastal areas and normalize the captured data. Based on the processed data, the impulse response function is used to analyze the relationship between different economic variables, so as to build the PSO-LSTM model, which is used to predict the economic growth trend of coastal areas. The experimental results show that, compared with the experimental comparison algorithm, the prediction accuracy of the algorithm designed in this paper is always above 97%, and the prediction time is always below 1 s, which has certain practical significance.

Examining optimum prediction time of rainfall dynamics based on chaotic perspective at different temporal scales: a case study in Bojonegoro, Indonesia

As the driving force of the hydrological system, rain has severe impact when dealing with petroleum mining activities, especially in protecting assets and safety. Rainfall has high variability, both spatial and temporal (chaotic data). Due to this reason, ones can only create long-range prediction using the stochastic method. Here we use the Lyapunov exponent to analyze the nonlinear pattern of rainfall dynamics. This method is useful for identifying chaotic deportment in rainfall data. This study uses rainfall data for six years obtained from one of the largest petroleum mining sites in Bojonegoro, Indonesia. Rainfall dynamics have been analyzed on three different time scales, namely daily data, 5-day, and 10-day. The time delay (τ) was obtained by using the Average Mutual Information (AMI) method for the three-rainfall series (3, 2, 3, respectively). The observed rainfall data in Bojonegoro show signs of chaos as the finite correlation dimensions (m) attain values about 4 for all time scales. The maximum Lyapunov exponent λmax for each of three-rainfall series in Bojonegoro is 0.111, 0.057, 0.062, respectively. These values were analyzed to find the optimum prediction time of rainfall occurrence to perform better forecasting. The result shows that the optimum range of prediction time for daily, 5-day, and 10-day have 9, 18, and 16 times longer than their temporal scale.