scholarly journals A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
C. L. Karmaker ◽  
P. K. Halder ◽  
E. Sarker
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Additive Model ◽  
Statistical Technique ◽  
Software Performance ◽  
Product Availability ◽  
Time Series Plot ◽  
Forecasting Errors ◽  
Jute Yarn

In today’s competitive environment, predicting sales for upcoming periods at right quantity is very crucial for ensuring product availability as well as improving customer satisfaction. This paper develops a model to identify the most appropriate method for prediction based on the least values of forecasting errors. Necessary sales data of jute yarn were collected from a jute product manufacturer industry in Bangladesh, namely, Akij Jute Mills, Akij Group Ltd., in Noapara, Jessore. Time series plot of demand data indicates that demand fluctuates over the period of time. In this paper, eight different forecasting techniques including simple moving average, single exponential smoothing, trend analysis, Winters method, and Holt’s method were performed by statistical technique using Minitab 17 software. Performance of all methods was evaluated on the basis of forecasting accuracy and the analysis shows that Winters additive model gives the best performance in terms of lowest error determinants. This work can be a guide for Bangladeshi manufacturers as well as other researchers to identify the most suitable forecasting technique for their industry.

scholarly journals Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

2021 ◽  
Vol 2 (23) ◽  
pp. 1-15
Author(s):  
Mwana Said Omar ◽  
◽  
Hajime Kawamukai
Keyword(s):  
Time Series ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Additive Model ◽  
Grid Search ◽  
Sarima Model ◽  
Ndvi Time Series ◽  
Sarima Models

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.

scholarly journals Challenges in the control of COVID-19 outbreaks caused by the delta variant during periods of low humidity: an observational study in Sydney, Australia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Michael P. Ward ◽  
Yuanhua Liu ◽  
Shuang Xiao ◽  
Zhijie Zhang
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Moving Average ◽  
Likelihood Ratio Statistic ◽  
Additive Model ◽  
Additive Models ◽  
Local Health ◽  
Weather Factors ◽  
Best Fitting ◽  
Sydney Australia

Abstract Background Since the appearance of severe acute respiratory coronavirus 2 (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) pandemic, a growing body of evidence has suggested that weather factors, particularly temperature and humidity, influence transmission. This relationship might differ for the recently emerged B.1.617.2 (delta) variant of SARS-CoV-2. Here we use data from an outbreak in Sydney, Australia that commenced in winter and time-series analysis to investigate the association between reported cases and temperature and relative humidity. Methods Between 16 June and 10 September 2021, the peak of the outbreak, there were 31,662 locally-acquired cases reported in five local health districts of Sydney, Australia. The associations between daily 9:00 am and 3:00 pm temperature (°C), relative humidity (%) and their difference, and a time series of reported daily cases were assessed using univariable and multivariable generalized additive models and a 14-day exponential moving average. Akaike information criterion (AIC) and the likelihood ratio statistic were used to compare different models and determine the best fitting model. A sensitivity analysis was performed by modifying the exponential moving average. Results During the 87-day time-series, relative humidity ranged widely (< 30–98%) and temperatures were mild (approximately 11–17 °C). The best-fitting (AIC: 1,119.64) generalized additive model included 14-day exponential moving averages of 9:00 am temperature (P < 0.001) and 9:00 am relative humidity (P < 0.001), and the interaction between these two weather variables (P < 0.001). Humidity was negatively associated with cases no matter whether temperature was high or low. The effect of lower relative humidity on increased cases was more pronounced below relative humidity of about 70%; below this threshold, not only were the effects of humidity pronounced but also the relationship between temperature and cases of the delta variant becomes apparent. Conclusions We suggest that the control of COVID-19 outbreaks, specifically those due to the delta variant, is particularly challenging during periods of the year with lower relative humidity and warmer temperatures. In addition to vaccination, stronger implementation of other interventions such as mask-wearing and social distancing might need to be considered during these higher risk periods. Graphical Abstract

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.

Abstract 345: Forecasting the Number of Out-Of-Hospital Cardiac Arrest in the Young Using Generalized Additive Model

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Alyssa Vermeulen ◽  
Marina Del Rios ◽  
Teri L Campbell ◽  
Hai Nguyen ◽  
Hoang H Nguyen
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Generalized Additive Model ◽  
Moving Average ◽  
Additive Model ◽  
Resource Planning ◽  
Model Framework ◽  
Auto Regressive ◽  
Hospital Cardiac Arrest ◽  
Causality Hypothesis

Introduction: Accurate forecasting could help in resource planning and evaluation of intervention efforts to reduce out-of-hospital cardiac arrest. Hypothesis: Generalized additive model can rapidly and accurately forecast the number of OHCA in the young (1-35 years old) when compared to other auto regressive moving average (ARIMA) based models. Methods: Data were obtained from CARES in Chicago from May 2013 to December 2017. Monthly forecasts of the number of OHCA were performed using a generative additive model framework using the open source software Prophet and R. The first 50 months served as training and the last 6 months served as testing. Results: Figure 1 shows the distribution of the number of cases over the 3-year study period showing yearly seasonality and upward trend. Figure 2 shows the forecast of the number of arrest (middle line) along with the actual number of arrest (dots). The shaded band represents the 95% confidence interval of the prediction. Figure 3 show the comparison of the 6-month forecast and actual data. This model has low root mean square error score when compared with other ARIMA based models (2 vs. 4). Conclusion: Accurate time series forecasting of the number of OHCA arrests could be achieved. Time series analysis and forecasting are essential tools in evaluating the effectiveness of intervention efforts to reduce OHCA as they allow for causality hypothesis testing.

scholarly journals Identifying an Appropriate Forecasting Technique for Predicting Future Demand: A Case Study on a Private University of Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 15-19
Author(s):  
Sayma Suraiya ◽  
M Babul Hasan
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Moving Average ◽  
Demand Forecasting ◽  
Private University ◽  
Planning Horizon ◽  
Time Series Forecasting ◽  
Base Level ◽  
Printing Paper

Demand forecasting and inventory control of printing paper is crucial that is frequently used every day for the different purposes in all sectors of educational area especially in Universities. A case study is conducted in a University store house to collect all historical demand data of printing papers for last 6 years (18 trimesters), from January (Spring) 2011 to December (Fall) 2016. We will use the different models of time series forecasting which always offers a steady base-level forecast and is good at handling regular demand patterns. The aim of the research paper is to find out the less and best error free forecasting techniques for the demand of printing paper for a particular time being by using the quantitative forecasting or time series forecasting models like weighted moving average, 3-point single moving average, 3-point double moving average, 5-point moving average, exponential smoothing, regression analysis/linear trend, Holt’s method and Winter’s method. According to the forecasting error measurement, we will observe in this research that the best forecasting technique is linear trend model. By using the quantities of data and drawing the conclusion with an acceptable accuracy, our analysis will help the university to decide how much inventory is absolutely needed for the planning horizon. Dhaka Univ. J. Sci. 66(1): 15-19, 2018 (January)

scholarly journals FORECASTING SEASONAL TIME SERIES DATA USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD OF ADDITIVE MODELS

2020 ◽  
Vol 16 (2) ◽  
pp. 151
Author(s):  
Nurhamidah Nurhamidah ◽  
Nusyirwan Nusyirwan ◽  
Ahmad Faisol
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Additive Model ◽  
Exponential Smoothing ◽  
Additive Models ◽  
Smoothing Method ◽  
Series Data ◽  
Seasonal Time Series ◽  
Exponential Smoothing Method ◽  
Forecasting Errors

The purpose of this study was to predict seasonal time series data using the Holt-Winters exponential smoothing additive model.  The data used in this study is data on the number of passengers departing at Hasanudin Airport in 2009-2019, the source of the data obtained from the official website of the Central Statistics Agency.  The results showed that the Holt-Winters exponential smoothing method on the passenger's number at Hasanudin Airport in 2009 to 2019 contained trend patterns and seasonal patterns, by first determining the initial values and smoothing parameters that could minimize forecasting errors.

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 Time series models: an analysis of demand forecast accuracy for a product line in a clothing company

2020 ◽  
Vol 15 (4) ◽  
pp. 219-251
Author(s):  
Guilherme Issao Chiba ◽  
Mônica Maria Mendes Luna
Keyword(s):  
Time Series ◽  
Qualitative Data ◽  
Demand Forecasting ◽  
Product Line ◽  
Time Series Models ◽  
Accuracy Evaluation ◽  
Forecasting Methods ◽  
The Impact ◽  
Forecasting Errors

Purpose – The purpose of this paper is to compare the performance of time series forecasting methods for a product line in a clothing company by analyzing the accuracy of demand forecasts Design/methodology/approach – This paper presents a case study in a large clothing company. Several methods were used to obtain both quantitative and qualitative data. Qualitative data were mainly used to describe the demand forecasting process and the quantitative data to make forecasts. Three time series models were applied to make forecasts and an accuracy analysis was done using different error measures. Findings – Regarding the three time series models applied in this case study, the static one is suitable for the product line considered, especially taking into account the impact of forecasting errors for carrying inventory and stockouts. We also identified advantages of quantitative methods and highlighted the importance of the forecast’s accuracy evaluation to choose an adequate model. Originality/value – There are few studies describing in detail the use of quantitative forecasting methods, specially addressing the forecasting process and error accuracy evaluation. This paper describes the use of three different time-series models to forecast the demand of the main product line in a large Brazilian clothing company. Furthermore, it suggests how to analyze the impact of forecasting errors on level inventory decisions and emphasizes forecast’s accuracy importance to support management decisions, a topic rarely addressed in the literature. Keywords - Demand forecasting. Time series forecast. Static methods. Winter Model.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average
Sign in / Sign up

Export Citation Format

Share Document