scholarly journals Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Chieh-Fan Chen ◽  
Wen-Hsien Ho ◽  
Huei-Yin Chou ◽  
Shu-Mei Yang ◽  
I-Te Chen ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Stock Market ◽  
Minimum Temperature ◽  
Moving Average ◽  
Arima Model ◽  
Maximum Temperature ◽  
Stock Market Index ◽  
Autoregressive Integrated Moving Average ◽  
Market Index ◽  
Independent Variable

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals A Forecasting Method Based on ARIMA Model for Best-Fitted Nutrition Water Supplement on Fruits

2019 ◽  
Vol 9 (2) ◽  
pp. 3167-3173
Keyword(s):  
Water Level ◽  
Moving Average ◽  
Arima Model ◽  
Environmental Parameters ◽  
Maximum Temperature ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Feature Vectors ◽  
Forecasting Method ◽  
Water Rate

The main focus of this research is to promote a forecasting method in the greenhouse of cultivation for the nutrition water level of strawberry fruits. In the greenhouse of cultivation, this study selects strawberry fruits as the focus on research. With adequate nutrition water supply conditions, the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA-SARIMA) were utilized to create forecasting for the nutrition water level of strawberry leaves in the fruit greenhouse of cultivation, thus forecasting strawberry's nutrition water rate through greenhouse environmental parameters. Next, the multi-scale feature vectors of greenhouse temperature and nutrition water parameters in the greenhouse have been extracted by using the data pre-processing method to eliminate the testing and training value of variables, thus improving the forecasting and generalization ability of the model. The extracted feature vectors have been used to train and optimize the SARIMA model, finally obtaining the forecasting model of nutrition water rate of strawberry fruits leaves in the greenhouse of cultivation, which has been compared in experiments with the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA - SARIMA) model. The results indicate that when training samples become a certain amount, the forecasting accuracy and regression fitting degree of ARIMA - SARIMA can be higher than that of the two traditional models. We forecasted that the strawberry greenhouse included 233 samples collected from a strawberry greenhouse in South Korea, and the 6 variables involved are greenhouse maximum temperature, greenhouse minimum temperature, greenhouse average temperature, quality of nutrient water, humanity, and CO2 , which would influence the strawberry growth in production concentration directly or indirectly with the variation of nutrition water every day.

scholarly journals An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhang Peng ◽  
Farman Ullah Khan ◽  
Faridoon Khan ◽  
Parvez Ahmed Shaikh ◽  
Dai Yonghong ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Stock Market ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Hybrid Models ◽  
Machine Learning Techniques ◽  
Stock Market Index

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.

scholarly journals Financial Forecasting Model in Developed and Developing Economies

2019 ◽  
Vol 8 (3S3) ◽  
pp. 291-296
Keyword(s):  
Stock Market ◽  
Developing Economies ◽  
Moving Average ◽  
Consumer Price Index ◽  
Absolute Error ◽  
Volatility Forecasting ◽  
Stock Market Index ◽  
Monthly Data ◽  
Forecasting Models ◽  
Market Index

The study focused on the volatility forecasting in developed and developing share market. The objective of the study was to evaluate the ability of six different statistical and econometric volatility forecasting models in the context of India, Brazil, Japan and US stock market from November 1994 till February 2005 on the basis of four evaluation error measures statistics which are mean absolute error (MAE), root mean square error (RMSE), Theil’s U (TU) and MAPE. The monthly data of stock market index of India, Brazil, Japan and US were collected from January 1992 till April 2005 and also monthly data of stock market index, discount rate, consumer price index (CPI), industrial production and foreign exchange reserves of India, Brazil, Japan and US respectively were collected. Then further analysis was done using four forecasting models which were moving average, exponential weighted moving average, multiple regression, GARCH. The study found out that GARCH and MAE forecasting models are superior in developed market as well as developing market like India.

scholarly journals Autoencoder Neural Network Approaches for Anomaly Detection in IBOVESPA Stock Market Index

2021 ◽  
Author(s):  
Lucas de Azevedo Takara ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Anomaly Detection ◽  
Moving Average ◽  
Reconstruction Error ◽  
Sao Paulo ◽  
São Paulo ◽  
Stock Market Index ◽  
Short Term ◽  
Market Index

Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. Anomaly detection has been applied to many problems such as bank fraud, fault detection, noise reduction, among many others. Some approaches to detect anomalies include classical statistical econometric methods such as AutoRegressive Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) approaches. More recently, with the progress of artificial intelligence and more specifically, machine learning, new algorithms such as one-class support vector machines, isolation forest, gradient boosting, and deep neural networks were applied to such tasks. This paper focuses on propose an anomaly detection framework for the Índice da Bolsa de Valores de São Paulo (IBOVESPA). It is a major stock market index that tracks the performance of around 50 most liquid stocks traded on the São Paulo Stock Exchange in Brazil. Exploring unsupervised autoencoder neural network algorithms, we compare the long short-term autoencoder, bidirectional long short-term autoencoder, and convolutional autoencoder models, aiming to explore the performance of these architectures for anomaly detection. Due to the ability of autoencoders to learn a compressed representation of their respective input, we train these models with standard data by minimizing the mean absolute error (MAE) loss function and evaluate them with anomalous inputs. We set a reconstruction error threshold, and in case that the reconstruction error of the test data sample is beyond it, anomalies are detected. Our results show that these models perform quite well and can be applied to real stock market data.

PERFORMANCE OF KUALA LUMPUR COMPOSITE INDEX STOCK MARKET

2020 ◽  
Vol 5 (2) ◽  
pp. 553
Author(s):  
Noreha Mohamed Yusof ◽  
Badrina Nur Yasmin Badrul Azhar ◽  
Syazana Zakaria ◽  
Intan Nadia Azvilla Maulad Mohamad Rawi
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Moving Average ◽  
Arima Model ◽  
Composite Index ◽  
Underlying Structure ◽  
Kuala Lumpur ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) is made up of over 30 large companies listed on the Bursa Malaysia Main Market. All FTSE Bursa Malaysia data are calculated and disseminated every 15 seconds in real-time. It is believed that the volatility of the stock market has a negative impact on real economic recovery. This paper aims to describe the underlying structure and the phenomenon of the sequence of observations in the series. The information obtained, can determine the performance of time series model to fit the data series from January 2002 until December 2018. Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been shown to provide the correct trend of volatility. The objectives of this paper are to determine the overall trend of the KLCI stock return and to investigate the performance of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) based on KLCI stock return. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been chosen to be used in this paper to measure accuracy. The results show that the best ARIMA model is ARIMA(1,1), while for the GARCH model, it is GARCH(1,1).

scholarly journals FORECASTING ONE-DECADE AHEAD MINIMUM TEMPERATURE AND RELATIVE HUMDITY FOR WATER RESOURCES MANAGEMENT IN LOWER NIGER

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Oluwatobi Aiyelokun ◽  
Abdulrahamon Olodo
Keyword(s):  
Water Resources ◽  
Relative Humidity ◽  
Water Resources Management ◽  
Minimum Temperature ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Resources Management ◽  
Weather Parameters ◽  
Water Resources Projects ◽  
Best Fit

Water availability is highly influenced by variability of weather parameters. Minimum temperature and relative humidity are important parameters that have been sidelined in many water resources management projects. In this study, Autoregressive Integrated Moving Average (ARIMA) models were identified and diagnosed in order to forecast mini-mum temperature and relative humidity of the study area. The findings of the study show that minimum temperature was high during dry season, when relative humidity was low. Furthermore, the multiplicative seasonal models best fit mini-mum temperature and relative humidity represented as ARIMA (5, 1, 0)(2, 0, 0)12 and ARIMA (1, 0, 0)(2, 0, 0)12 respec-tively. While, a ten-year forecast derived from the models would be useful for effective planning and acquisition of water resources projects in the study area.

scholarly journals ARIMA Model in Predicting Banking Stock Market Data

2018 ◽  
Vol 12 (11) ◽  
pp. 309 ◽  
Author(s):  
Mohammad Almasarweh ◽  
S. AL Wadi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Short Term Prediction

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

scholarly journals Picking Buy-Sell Signals: A Practitioner’s Perspective on Key Technical Indicators for Selected Indian Firms

2019 ◽  
Vol 14 (3) ◽  
pp. 205-219 ◽  
Author(s):  
Talwar Shalini ◽  
Shah Pranav ◽  
Shah Utkarsh
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Moving Average ◽  
Relative Strength ◽  
Entry And Exit ◽  
Stock Market Index ◽  
Listed Firms ◽  
Market Index ◽  
Portfolio Managers ◽  
Good Risk

AbstractThe purpose of this study is to undertake technical analysis of selected companies included in the S&P CNX Nifty 50, a leading stock market index in India. We have used the stock price data of twenty leading listed firms in India for a period from January 1, 2012 through December 31, 2017. We have applied Guppy Multiple Moving Average (GMMA), Moving Average Convergence Divergence (MACD), Stochastic Relative Strength Index (Stoch RSI) and Average Directional Index (ADX) to Heikin Ashi charts to back test and provide entry and exit points for the players in the stock market. Analysis of the price information has revealed that the GMMA and ADX are effective indicators for most of the stocks under the study but they give late signals as compared to RSI and MACD. Further, the study has shown that though RSI and MACD give early signals, yet they are risky as the number of false signals generated by them is also found out to be quite high. The study is important as the findings can be used by investors, option traders and portfolio managers to get generate profitable trading signals and obtain good risk to reward ratios.

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