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.

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

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.

scholarly journals Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

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.

scholarly journals An improved ARIMA model for hydrological simulations

2014 ◽  
Vol 1 (1) ◽  
pp. 841-876 ◽  
Author(s):  
H. R. Wang ◽  
C. Wang ◽  
X. Lin ◽  
J. Kang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Series Data ◽  
Monthly Data ◽  
Annual Variations ◽  
Characteristic Quantity ◽  
Improved Model

Abstract. Auto Regressive Integrated Moving Average (ARIMA) model is often used to calculate time series data formed by inter-annual variations of monthly data. However, the influence brought about by inter-monthly variations within each year is ignored. Based on the monthly data classified by clustering analysis, the characteristics of time series data are extracted. An improved ARIMA model is developed accounting for both the inter-annual and inter-monthly variation. The correlation between characteristic quantity and monthly data within each year is constructed by regression analysis first. The model can be used for predicting characteristic quantity followed by the stationary treatment for characteristic quantity time series by difference. A case study is conducted to predict the precipitation in Lanzhou precipitation station, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

scholarly journals Comparing of ARIMA and RBFNN for short-term forecasting

2015 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Ahmad Jawahir
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Forecasting ◽  
Simulated Time ◽  
Modelling And Forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.

scholarly journals Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur

2021 ◽  
Vol 3 (1) ◽  
pp. 27-32
Author(s):  
Yoesril Ihza Mahendra ◽  
Natalia Damastuti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Economic Value ◽  
Series Data ◽  
Model Parameters ◽  
Marine Animal ◽  
Autoregressive Integrated Moving Average ◽  
Tiger Shrimp

Prediction of demand for tiger shrimp buyers using data from the company CV. Surya Perdana Benur. The process is carried out with the models in the Autoregressive Integrated Moving Average method. Tiger shrimp is a marine animal that is now widely cultivated by big company in Indonesia. Tiger shrimp has important economic value, so its existence must be maintained as part of Indonesian germplasm. The problem now faced by many tiger shrimp companies is the inadequate availability of goods for consumers. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. Autoregressive (AR), MovingAverage (MA), and Autoregressive Integrated Moving Average (ARIMA) model and will be evaluated through Mean Absolute Percent Error (MAPE). The initial process that will be carried out after the data is processed is model identification, estimation of model parameters, residual inspection, using forecasting models if the model has been fulfilled will be evaluated using MAPE until the results come out 14875.593875 to be able to predict the next buyer demand.

scholarly journals PERBANDINGAN ANTARA MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN MODEL FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN DI KABUPATEN MANOKWARI

2019 ◽  
Vol 15 (2) ◽  
pp. 78-87
Author(s):  
Esther Ria Matulessy
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Transfer Function Model ◽  
Function Model ◽  
Autoregressive Integrated Moving Average ◽  
West Papua ◽  
Input Variables

This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).

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