scholarly journals Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

2021 ◽  
Vol 2115 (1) ◽  
pp. 012044
Author(s):  
R. Vaibhava Lakshmi ◽  
S. Radha
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Auto Correlation ◽  
Correlation Plot ◽  
Best Fitting ◽  
Fitting Model

Abstract The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.

scholarly journals Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models

2020 ◽  
Vol 63 (5) ◽  
Author(s):  
Dulin Zhai ◽  
Xueming Zhang ◽  
Pan Xiong
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Time Series Data ◽  
Arima Model ◽  
Longwave Radiation ◽  
Time Series Forecasting ◽  
Series Data ◽  
Jiuzhaigou Earthquake ◽  
Forecasting Models ◽  
Mexico Earthquake

  The catastrophic damages caused by the Jiuzhaigou earthquake in China of August 8, 2017 and the Mexico earthquake of September 20, 2017 have revealed some important weaknesses of currently operational earthquake-monitoring and forecasting systems. In this work, six time series forecasting models were applied to detect pre-earthquake anomalies within infrared outgoing longwave radiation. After comparing their prediction results using non-seismic time series data, the autoregressive integrated moving average (ARIMA) model was selected as the optimal model, and then a new prediction method based on this ARIMA model was proposed. The results show that the values observed on July 27 and August 5 before the Jiuzhaigou earthquake in China exceed the confidence interval for prediction and reaches the maximum on August 5, 2017. This indicates the infrared outgoing longwave radiation (IR-OLR) anomalies before the Jiuzhaigou earthquake in China. For the Mexico earthquake, pre-earthquake IR-OLR anomalies are detected on September 14, 18, and 19, and reaches the maximum on September 14, 2017. This demonstrates that the proposed time series forecasting model based on ARIMA could be an effective method for earthquake anomalies detection within infrared outgoing longwave radiation.

scholarly journals Forecasting Stock Prices of PT. Bank Negara Indonesia (Persero) Tbk., by Method (BOX-JENKINS)

2021 ◽  
Vol 19 (1) ◽  
pp. 191
Author(s):  
Paiaman Pardede ◽  
Maurits Sipahutar ◽  
Parulian Naibaho
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Arima Model ◽  
Model Analysis ◽  
Equation Model ◽  
Analytical Tool ◽  
Share Price ◽  
Series Data

The purpose of this study is to find the most appropriate model for predicting future stock prices, and the analytical tool used is ARIMA. In this study, the authors used the time series data of the share price of PT BNI (Persero) Tbk. from January 3, 2017, to June 28, 2019, consisting of 594 working days from the Investing.com database. The research found that the ARIMA model analysis (3,1,3) is the most appropriate model for predicting the share price of PT. Bank Negara Indonesia (Persero) Tbk, with the equation model: Yt = - 6.331988 + 1.714721Yt-1 - 0.149406 Yt-2 - 1.72221 Y t-3 + 0.858083 Yt-4 + 0.729283 t-1 - 0.845787 t-2 - 0.898101 t-3.

scholarly journals Multiplicative Error Modeling Approach for Time Series Forecasting

2020 ◽  
Author(s):  
Poulami Chowdhury ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Error Modeling ◽  
Series Data ◽  
Data Sets ◽  
Multiplicative Error ◽  
Modeling Approach ◽  
Linear And Nonlinear

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

Time Series Data Forecasting

2011 ◽  
pp. 1125-1129
Author(s):  
Vincent Cho
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Stock Prices ◽  
New Technologies ◽  
Time Series Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Business Economics ◽  
Hourly Temperature ◽  
Employment Rates

Businesses are recognizing the value of data as a strategic asset. This is reflected by the high degree of interest in new technologies such as data mining. Corporations in banking, insurance, retail, and healthcare are harnessing aggregated operational data to help understand and run their businesses (Brockett et al., 1997; Delmater & Hamcock, 2001). Analysts use data-mining techniques to extract business information that enables better decision making (Cho et al., 1998; Cho & Wüthrich, 2002). In particular, time series forecasting is one of the major focuses in data mining. Time series forecasting is used in a variety of fields, such as agriculture, business, economics, engineering, geophysics, medical studies, meteorology, and social sciences. A time series is a sequence of data ordered in time, such as hourly temperature, daily stock prices, monthly sales, quarterly employment rates, yearly population changes, and so forth.

scholarly journals Stock Market Forecasting Technique using Arima Model

2020 ◽  
Vol 8 (6) ◽  
pp. 2694-2697
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Special Property ◽  
Daily Basis ◽  
Series Data ◽  
Predictive Algorithm ◽  
Stock Market Forecasting

In Financial market, various shares, bonds, securities or currencies are traded on the daily basis , thus making most of the datasets as time series data where price is plotted against a time series[11] . There are many techniques and analysis technique that can be used with times series data like ARIMA model, exponential Smoothing, Neural Networks or Simple Moving average. However ARIMA Model is commonly used to understand time series analysis in order to extract meaningful characteristics of the data and help in the prediction of the stock prices.[12] since it helps to understand what happened in past and past behavior of data can help to predict future. Time series is a special property and different set of predictive algorithm. There are three variants of the ARIMA Model namely Basic, Trend Based and Wavelet Based. In this paper key components of time series data have been discussed and implemented using ARIMA model for we have collected NIFTY daily data of Nifty50 index and wants to predict future value of the Stock.

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals BLACK SWAN GLOBAL MARKET: ANALYSIS OF THE EFFECT OF THE COVID-19 DEATH RATE ON THE VOLATILITY OF EUROPEAN FOOTBALL CLUB STOCK PRICES (CASE STUDY OF JUVENTUS F.C., MANCHESTER UNITED, AJAX AMSTERDAM AND BORUSSIA DORTMUND)

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Franco Benony Limba ◽  
Jacobus Cliff Diky Rijoly ◽  
Margreath I Tarangi
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Global Market ◽  
Bad News ◽  
Series Data ◽  
Black Swan ◽  
Financial Industry ◽  
Market Effect ◽  
European Football

Abstract: The Covid-19 pandemic that hit the world also directly affected financial markets and global stock markets; this condition in economic terminology is known as the Black Swann Global Market Effect. Black Swan Global Market Effect is also experienced by sports industries in the financial industry, the football industry. The purpose of this paper is to see whether there is an influence between the Covid-19 pandemic conditions on the share value of several major European football clubs, namely Ajax Amsterdam, Borussia Dortmund, Juventus F.C., and Manchester United, as a result of the Black Swan Global Market Effect. The data used in this paper is time-series data from March 2020 to August 2020. Meanwhile, to answer the black swan effect phenomenon, the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) method is used. The results showed that stocks that were the object of research (Ajax, Borussia Dortmund, Juventus, and Machester United) showed a large response to bad News (an increase in deaths due to covid-19). Abstrak:Pandemic covid-19 yang mengantam dunia juga secara langsung mempengaruhi pasar keuangan serta pasar saham global, kondisi ini dalam terminology ekonomi dikenal sebagai Black Swann Global Markert Effect. Black Swan Global Market Effect hal ini juga dialami industry-industri olahraga yang berada dalam industry keuangan tersebut salah satunya industry sepakbola.Tujuan penulisan ini adalah untuk melihat apakah terdapat pengaruh antara kondisi pandemic covid-19 terhadap nilai saham beberapa klub sepakbola besar eropa yaitu Ajax Amsterdam, Borussia Dortmund, Juventus FC, dan Manchester United sebagai akibat dari Black Swan Global Market Effect.Data yang digunakan dalam penulisan ini adalah data time series dari bulan maret 2020 hingga agustus 2020. Sementara untuk menjawab fenomoena black swan effect ini digunakan metode Threshold Generalized Autoregressive Conditional Heteroskedacity (TGARCH). Hasil Penelitian menunjukkan bahwa, saham-saham yang menjadi objek penelitian (Ajax, Borussia Dortmund, Juventus, dan Machester United) menunjukan respons yang besar terhadap bad news (peningkatan jumlah kematian akibat covid-19). Black Swan Global Market, Pandemi Covid-19, TGARCH Models

scholarly journals Partial Correlation-Based Attention for Multivariate Time Series Forecasting

2020 ◽  
Vol 34 (10) ◽  
pp. 13720-13721
Author(s):  
Won Kyung Lee
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Series Data ◽  
Time Lags ◽  
Dependency Structure ◽  
Agnostic Learning

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

scholarly journals Market model analysis and forecasting behavior of watermelon production in Bangladesh

2014 ◽  
Vol 26 (1-2) ◽  
pp. 47-56
Author(s):  
Murshida Khanam ◽  
Umme Hafsa
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Linear Models ◽  
Time Series Data ◽  
Arima Model ◽  
Model Analysis ◽  
Market Model ◽  
Series Data ◽  
Log Linear

An attempt has been made to study various models regarding watermelon production in Bangladesh and to identify the best model that may be used for forecasting purposes. Here, supply, log linear, ARIMA, MARMA models have been used to do a statistical analysis and forecasting behavior of production of watermelon in Bangladesh by using time series data covering whole Bangladesh. It has been found that, between the supply and log linear models; log linear is the best model. Comparing ARIMA and MARMA models it has been concluded that ARIMA model is the best for forecasting purposes. DOI: http://dx.doi.org/10.3329/bjsr.v26i1-2.20230 Bangladesh J. Sci. Res. 26(1-2): 47-56, December-2013

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