scholarly journals Prediction-based adaptive compositional model for seasonal time series analysis

2017 ◽  
Vol 36 (7) ◽  
pp. 842-853
Author(s):  
Kun Chang ◽  
Rong Chen ◽  
Thomas B. Fomby
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Compositional Model ◽  
Series Analysis ◽  
Seasonal Time Series

scholarly journals Short Term Prediction of PM10Concentrations Using Seasonal Time Series Analysis

2016 ◽  
Vol 47 ◽  
pp. 05001 ◽  
Author(s):  
Hazrul Abdul Hamid ◽  
Ahmad Shukri Yahaya ◽  
Nor Azam Ramli ◽  
Ahmad Zia Ul-Saufie ◽  
Mohd Norazam Yasin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Short Term ◽  
Series Analysis ◽  
Seasonal Time Series ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Implementasi Spasial Kriging Dengan Faktor Dependency Seasonal Time Series

2016 ◽  
Vol 1 (2) ◽  
pp. 37
Author(s):  
Aniq Atiqi Rohmawati
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Oil Palm ◽  
Pearson Correlation ◽  
Arima Model ◽  
Spatial Location ◽  
Kriging Interpolation ◽  
Series Analysis ◽  
Oil Palm Plantation ◽  
Seasonal Time Series

<p>Time series analysis has been developed in concepts and theories to accommodate the behavior of the collected data by involving time. The unique feature of time series analysis is the time dependency. In this research, we observed a number of seasonal pets, fire caterpillars, on an oil palm plantation at Block Afdeling-D in Kalimantan. The number of Fire Caterpillars is dependent on time and spatial (location). Fire Caterpillars are seasonal pests on oil palm plantation. In addition, Pearson correlation indicates that the number of Fire Caterpillars is not influenced by the distance among the blocks. We suggests that the disinfection should be done simultaneously to avoid the migration of fire caterpillars. The spreading of fire caterpillars at Block Afdeling-D in Kalimantan is modeled with time series seasonal model, spesifically with ARIMA homoscedastic model. Kriging interpolation was conducted to identify behavior and determine the location Fire Caterpillars involving ARIMA model.</p><strong>Keywords: </strong>ARIMA, dependency, Kriging, Fire Caterpillars, variogram

Modified Trend and Seasonal Time Series Analysis for Operations: A Case Study of Soft Drink Production

2012 ◽  
Vol 7 ◽  
pp. 63-72
Author(s):  
Harold C. Godwin ◽  
C.E. Okafor
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Trend ◽  
Soft Drink ◽  
Decomposition Analysis ◽  
Stationary Time Series ◽  
Series Analysis ◽  
Seasonal Time Series ◽  
Demand Rate

Many Production and Business Time Series Are Non-Stationary Time Series that Contain Trend and Seasonal Variations. Seasonality Is a Periodic and Recurrent Pattern Caused by Factors such as Weather, Holidays, or Repeating Promotions. this Paper Presents a Trend and Seasonal Time Series Analysis of Soft Drink Production over the Period 2003–2010, it Is Necessary to Know the Trend in Soft Drink Production to Elicit the Reasons why Demand of Soft Drink Is Increased or Decreased at Specific Periods. the Objectives of this Paper Are (i) to Study the Trends in the Production and Productivity of a Soft Drink Bottling Company, and (ii) Analyze the Demand of the Firm with a View to Identifying Trend that Exists in the Company Using Time Series Analysis. A Software Program Was Developed Based on Applicable Methodology to Facilitate Accurate and Faster Analysis of Data. Characterization of Demand Data Using Decomposition Was Done, which Reveal the Nature of Seasonality, Cyclical Activity, Trend and Noise. on the Whole, the Results of the Decomposition Analysis Clearly Show that there Is a Remarkable Linear Trend in Demand Pattern. the Study of Seasonality Shows that the Highest Peak in Demand of the Product Occurred at 12th, 24th, 36th, 48th 60th, 72nd, 84th and 96th Months which Turn Out to Coincide with Yuletide. the Study Further Indicated a Positively Increasing Trend in the Demand Rate of Company’s Product.

Sign in / Sign up

Export Citation Format

Share Document