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 Examination of Temperature Variability over Lahore (Pakistan) and Dhaka (Bangladesh): A Comparative Study

2020 ◽  
Vol 10 (4) ◽  
pp. 46-50
Author(s):  
Khadija Shakrullah ◽  
Safdar Ali Shirazi ◽  
Sajjad Hussain Sajjad ◽  
Zartab Jahan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Temperature Variability ◽  
Series Data ◽  
Maximum Temperature ◽  
Average Model ◽  
Temperature Variations ◽  
Moving Average Model ◽  
Mega Cities

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

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 Examination of Temperature Variability over Lahore (Pakistan) and Dhaka (Bangladesh): A Comparative Study

2020 ◽  
Vol 10 (4) ◽  
pp. 46-50
Author(s):  
Khadija Shakrullah ◽  
Safdar Ali Shirazi ◽  
Sajjad Hussain Sajjad ◽  
Zartab Jahan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Temperature Variability ◽  
Series Data ◽  
Maximum Temperature ◽  
Average Model ◽  
Temperature Variations ◽  
Moving Average Model ◽  
Mega Cities

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.

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.  

Testing for Instrumentation in Transportation Time Series Data: A Case Study

1997 ◽  
Vol 1581 (1) ◽  
pp. 89-92
Author(s):  
Steven M. Rock
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Definition Of ◽  
Accident Statistics ◽  
Percent Decline

Instrumentation is one of the threats to the validity of experiments. Four possible cases of instrumentation in a time series of traffic accident statistics in Illinois since the mid-1970s were tested, primarily by using autoregressive integrated moving average methods. Two of these cases, a 1977 change in the reporting threshold for property-damage-only (PDO) accidents and a 1989 change in the definition of a fatality, were not found to be significant. A 1989 change in the method of tabulating monthly data and a 1992 change in the reporting threshold for PDO accidents were statistically significant. These two cases combined could account for a more than 15 percent decline in PDO accidents.

Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 43-50
Author(s):  
Umi Mahmudah
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Forecasting Analysis ◽  
Graduate Unemployment ◽  
Point Forecast

AbstractNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.

scholarly journals Forecasting cotton lint exports in Nigeria using the autoregressive integrated moving average model

2021 ◽  
Vol 19 (1) ◽  
pp. 150-162
Author(s):  
A.S. Akenbor ◽  
P.I. Nwandu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Cotton Lint ◽  
Colonial Era ◽  
Autoregressive Integrated Moving Average ◽  
Post Colonial ◽  
Moving Average Model ◽  
Oil Sector

Nigeria was a major global exporter of cotton lint to international market during the colonial and post-colonial era till late 70s when the  country fully embraced oil exports to the detriment of the non-oil sector, cotton lint exports inclusive. However, Nigeria is gradually emphasizing agricultural exports again to earn huge foreign exchange, the oil sector having left the country in economic crises. This study utilized time series model particularly, Autoregressive Integrated Moving Average (ARIMA) to make forecasting of cotton lint exports in Nigeria by using 46 yearly observations (1970-2015). The model went through series of investigative and diagnostic tests in order to observe the usefulness of the model. The fitting of the selected ARIMA (2,1,2) model to the time series data, means fitting ARIMA (2,1,2) model of one first order difference. Smaller RMSE, MAE as well as Theil Inequality coefficient are actually preferred and justified that ARIMA (2,1,2) model was justified as adequate for the forecasting of cotton lint exports in Nigeria with AIC value of 20.96771, SIC value of 21.04881, MAPE value of 6751.231, RMSE of 93303.67 and R2 of 0.330951. A thirty-year period ahead of cotton lint exports is predicted. The observations signify a rising trend in exports hence; it will be available especially in the future for foreign trade in the next thirty years. The outcome from the study is valuable for trade organisations and investors in assessing the precariousness of the market structure.

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