A Pragmatic Study on Time Series Models for Big Data

2018 ◽  
Vol 6 (8) ◽  
pp. 67
Author(s):  
B. Arputhamary ◽  
L. Arockiam
Keyword(s):  
Time Series ◽  
Big Data ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Moving Average ◽  
Series Data ◽  
Bike Sharing ◽  
Auto Regressive ◽  
Changes Over Time ◽  
The Given

Recent years have witnessed the growth of Big Data, particularly Time Series data which initiates major research interest in Time Series analysis and forecasting future values. It finds interest in many applications such as business, stock market and exchange, weather forecasting, electricity demand, cost and usage of products and in any kind of place that has specific seasonal or trendy changes over time. The forecasting of Time Series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed study is performed to find the total number of bike users with respect to the season and weather on Capital Bike Sharing System (CBS) dataset. The study covers the Auto Regressive Integrated Moving Average (ARIMA), Holt-Winters Additive and Multiplicative forecasting models to analyse the seasonal and trendy fluctuations of the given dataset to improve performance and accuracy.

scholarly journals Food Predictive Anatomization using Time Series

2020 ◽  
Vol 9 (9) ◽  
pp. 14-17
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Moving Average ◽  
Series Data ◽  
Market Exchange ◽  
Average Model ◽  
Moving Average Model ◽  
Auto Regressive

Time series survey and forecasting upcoming values has been a research focus past years ago. Time series analysis and predict The time-series data finds its importance in various roles of implementation such as business, stock market exchange, weather forecasting, electricity demand, cost and usage of products such as fuels, etc. In this project, a detailed survey of the various techniques applied for forecasting different method of time series datasets are provided. Moving average model and Auto-Regressive Integrated Moving Average model with a case study on food predictive analysis time series data with R software.

scholarly journals PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG

2021 ◽  
Vol 10 (2) ◽  
pp. 279-292
Author(s):  
Rezky Dwi Hanifa ◽  
Mustafid Mustafid ◽  
Arief Rachman Hakim
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Moving Average ◽  
Financial Data ◽  
Series Data ◽  
Leverage Effect ◽  
Standard Equipment ◽  
Conditional Heteroscedasticity ◽  
Auto Regressive

Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, and homogeneous. However, usually in financial data, the residual variants are not constant. This can be overcome by modeling variants. Standard equipment that can be used to model variants is the ARCH / GARCH (Auto Regressive Conditional Heteroscedasticity / Generalized Auto Regressive Conditional Heteroscedasticity) model. Another phenomenon that often occurs in GARCH models is the leverage effect on the residuals of the model. EGARCH (Exponential General Auto Regessive Conditional Heteroscedasticity) is a development of the GARCH model that is appropriate for data that has an leverage effect. The implementation of this model is by modeling financial data, so this study takes 136 monthly data on rice prices in Semarang City from January 2009 to April 2020. The purpose of this study is to create a long memory data forecasting model using the Exponential method. Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The best model obtained is ARFIMA (1, d, 1) EGARCH (1,1) which is capable of forecasting with a MAPE value of 3.37%.Keyword : Rice price, forecasting , long memory, leverage effect, GARCH, EGARCH

Modelling Monthly Volatility of the Muscat Securities Market (MSM) Index Using Auto Regressive Integrated Moving Average (ARIMA)

2019 ◽  
Vol 10 (11) ◽  
pp. 1045-1056
Author(s):  
Shaik Nafeez Umar Shaik ◽  
◽  
Labeeb Mohammed Zeeshan ◽  
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Moving Average ◽  
Performance Model ◽  
Securities Market ◽  
Series Data ◽  
Arima Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

The Stock market is eyewitness’s responsive activities and is gradually more gaining importance. The purpose of the study is to measure the volatility of selected emerging indices Muscat Securities Market (MSM). Time series analysis techniques were used including Auto Regressive Integrated Moving Average (ARIMA) models. The time series data considered of this study taken MSM 30. The study period has taken from January 2013 to December 2018 except Sharia-compliant index would be June 2013 to December 2018. Tools used for the study is Unit Toot Test (Augmented Dickey–Fuller and Phillips-Perron), ARIMA models and for performance model using Theil’s U-Statistic. The study made a few observations which may help the investors and model builders to understand better about the stock market.

scholarly journals Forecast of weather parameters using time series data

MAUSAM ◽  
2021 ◽  
Vol 65 (4) ◽  
pp. 509-520
Author(s):  
A.K. SHUKLA ◽  
Y.A. GARDE ◽  
INA JAIN
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Moving Average ◽  
Kendall Test ◽  
Weather Data ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Parameters

The present study is undertaken to develop area specific weather forecasting models based on time series data for Pantnagar, Uttarakhand. The study was carried out by using time series secondary monthly weather data of 27 years (from 1981-82 to 2007-08). The trend analysis of weather parameters was done by Mann-Kendall test statistics. The methodologies adopted to forecast weather parameters were the winter’s exponential smoothing model and Seasonal Autoregressive Integrated Moving Average (SARIMA). Comparative study has been carried out by using forecast error percentage and mean square error. The study showed that knowledge of this trend is likely to be helpful in planning and production of enterprises/crops. The study of forecast models revealed that SARIMA model is the most efficient model for forecasting of monthly maximum temperature, monthly minimum temperature and monthly humidity I. The Winter’s model was found to be the most efficient model for forecasting Monthly Humidity II but no model was found to be appropriate to forecast monthly total rainfall.

scholarly journals Application of ARIMA Model for River Discharges Analysis

2017 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Bhola NS Ghimire
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Hydrological Data ◽  
Time Component ◽  
Data Points ◽  
Auto Regressive ◽  
Component Time

<p class="Default">Time series data often arise when monitoring hydrological processes. Most of the hydrological data are time related and directly or indirectly their analysis related with time component. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Many methods and approaches for formulating time series forecasting models are available in literature. This study will give a brief overview of auto-regressive integrated moving average (ARIMA) process and its application to forecast the river discharges for a river. The developed ARIMA model is tested successfully for two hydrological stations for a river in US.</p><p><strong>Journal of Nepal Physical Society</strong><em><br /></em>Volume 4, Issue 1, February 2017, Page: 27-32</p>

scholarly journals Forecasting daily meteorological time series using ARIMA and regression models

2018 ◽  
Vol 32 (2) ◽  
pp. 253-264 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Magdalena Gos ◽  
Jaromir Krzyszczak
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
R Software ◽  
Time Series Regression ◽  
Climatic Zones ◽  
Autoregressive Integrated Moving Average ◽  
Temperature And Precipitation ◽  
Auto Regressive

Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

scholarly journals On Modeling Murder Crimes in Nigeria

2018 ◽  
pp. 157-162
Author(s):  
Obubu Maxwell ◽  
Ikediuwa Udoka Chinedu ◽  
Anabike Charles Ifeanyi ◽  
Nwokike Chukwudike C
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Data Set ◽  
Adequate Model ◽  
Resource Center ◽  
Moving Average Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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.  

Sign in / Sign up

Export Citation Format

Share Document