Stock trend prediction using simple moving average supported by news classification

2014 ◽  
Author(s):  
Stefan Lauren ◽  
S. Dra. Harlili
Keyword(s):  
Moving Average ◽  
Trend Prediction

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

scholarly journals Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 484-486
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Random Forest Algorithm ◽  
Strength Index ◽  
Training Set ◽  
Trend Prediction ◽  
Unique Role

Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.

scholarly journals Application of network theory to study spatio-temporal evolution in the weekend effect in urban areas

2021 ◽  
Author(s):  
Iván Y. Hernández-Paniagua ◽  
Rodrigo López Farías ◽  
Juan A. Pichardo Corpus
Keyword(s):  
Urban Areas ◽  
Prediction Models ◽  
Control Strategies ◽  
Moving Average ◽  
Ground Level ◽  
Temporal Variations ◽  
Weekend Effect ◽  
Anthropogenic Activity ◽  
Trend Prediction ◽  
Spatio Temporal

The occurrence of higher ground-level O3 concentrations on weekends rather than on weekdays, despite reduced anthropogenic activity in urban areas, is known as the O3 weekend effect (OWE). Here, we present an approach to analyse OWE spatio-temporal variations in urban areas, integrated by the trend, prediction and network representation. We used data from ten monitoring sites geographically distributed within the Mexico City Metropolitan Area (MCMA) recorded during 1994-2018. The OWE occurrence within the MCMA ranged typically between 40 and 60 % of the total weeks per year. The annual differences between weekday and weekend O3 peaks (magnitudes) showed were most significant on Sundays. Naive, Linear and Auto-regressive Integrated Moving Average models were tested for predicting the OWE annual occurrences and magnitudes. There was no single model that outperformed significantly for predicting OWE at all sites. The proposed concept of generalised OWE (GOWE) implies that at least half of the sites under study exhibited simultaneous OWE occurrence. GOWE is represented as a network and its integration with prediction models is useful to determinate the OWE spread over the MCMA in the following years. The GOWE occurrence showed an increasing trend interpreted as the spread of VOC-limited conditions over most of the MCMA. Predicted data suggest that, with the current emission control policies, the GOWE will continue occurring. The integrated methodology presented permits the acquisition of valuable insights into the design of potential air quality control strategies.

A Real-Time Twitter Trend Analysis and Visualization Framework

2019 ◽  
Vol 15 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Jamuna S Murthy ◽  
Siddesh G.M. ◽  
Srinivasa K.G.
Keyword(s):  
Trend Analysis ◽  
Real Time ◽  
Web Application ◽  
Recommendation System ◽  
Moving Average ◽  
Research Work ◽  
Trend Prediction ◽  
Emerging Trends ◽  
Wide Range ◽  
Time Update

Trend analysis over Twitter offers organizations a fast and effective way of predicting the future trends. In the recent years, a wide range of indicators and methods were used for predicting the trend on Twitter with varying results, unfortunately most of the research focused only on the emerging trends which has gained long-term attention on the Twitter platform. This article depicts trend variations, i.e. to predict whether the trend on Twitter will gain attention or not in the next few hours. Hence a novel method called: “Twitter Trend Momentum (TTM)” is introduced for trend prediction which is the enhancement of a well-known stock market indicator called moving average convergence divergence (MACD). Reason analysis for trend variation is also carried out as an extension to the authors' research work. An evaluation of the framework showed the best results which are applied to build a real-time web application called “TwitTrend.” The application acts as a real-time update and recommendation system of top trends to users.

scholarly journals Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5841
Author(s):  
Shan-Jen Cheng ◽  
Wen-Ken Li ◽  
Te-Jen Chang ◽  
Chang-Hung Hsu
Keyword(s):  
Neural Network ◽  
Linear Models ◽  
Moving Average ◽  
Absolute Error ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Trend Prediction ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
The Neural Network

Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.

scholarly journals Trend prediction of COVID-19 based on ARIMA model in mainland of China

2020 ◽  
Author(s):  
Han Chuqiao ◽  
Ju xifeng ◽  
zheng jianghua
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Test Group ◽  
Trend Prediction ◽  
Autoregressive Integrated Moving Average ◽  
Group Data ◽  
Predicted Values ◽  
Relative Errors ◽  
And Control ◽  
Experimental Group

Abstract: The ongoing pandemic of COVID-19 has aroused widespread concern around the world and poses a severe threat to public health worldwide. In this paper, the autoregressive integrated moving average (ARIMA) model was used to predict the epidemic trend of COVID-19 in mainland of China. We collected the cumulative cases, cumulative deaths, and cumulative recovery in mainland of China from January 20 to June 30, 2020, and divided the data into experimental group and test group. The ARIMA model was fitted with the experimental group data, and the optimal model was selected for prediction analysis. The predicted data were compared with the test group. The average relative errors of actual cumulative cases, deaths, recovery and predicted values in each province are between -22.32%-22.66%, -9.52%-0.08%, -8.84%-1.16, the results of the comprehensive experimental group and test group show The error of fitting and prediction is small, the degree of fitting is good, the model supports and is suitable for the prediction of the epidemic situation, which has practical guiding significance for the prevention and control of the epidemic situation.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average
Sign in / Sign up

Export Citation Format

Share Document