Improving forecasts for noisy geographic time series

Huddleston, Samuel H.; Porter, John H.; Brown, Donald E.

doi:10.1016/j.jbusres.2015.03.040

Cited by 15 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The crime rate also has emotional implications for locals and newcomers. The authors of [6,19,20] also highlight such implications from crime, emphasizing the promotion of analysis and the need for the standardization of both data and survey-based indicators of crime indexes to cater to the interests of stakeholders in public safety and security. Furthermore, it is important to compare the seasonality and non-seasonality-based forecasting methods ARIMA and GPF-ARIMA (Geographic probability method: a new geographic/spatial time series method introduced by [21] as the most suitable forecasting method with the lowest scaling error) [9].…”

Section: Brief Methodological Review Of the Literaturementioning

confidence: 99%

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

2023

View full text Add to dashboard Cite

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.

show abstract

Section: Brief Methodological Review Of the Literaturementioning

confidence: 99%

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

2023

View full text Add to dashboard Cite

show abstract

“…Then, the result is modelling assumption which was to get the particular forecast it obligates the type of historical forecast is more significant than the statistical behaviour of the model error. Besides that, the study that has been carried out for comparison of the performance of numerous simple top-down forecasting methods for predicting the noisy geographic time series to the performance of three methods which is a Naïve method, Holt-Winters method and Box Jenkins method (Huddleston, Porter, & Brown, 2015). This study had been applied in the city of Pittsburgh over the five years which is the comparison modelling performance of produce the forecast in a regularly weeks and patrol level sector of burglaries.…”

Section: The Possibilities Of Using Holt-winters Methods For Forecasting Tourist Arrivalsmentioning

confidence: 99%

Forecasting International Tourist Arrivals in Malaysia Using Sarima and Holt-Winters Model

Lip

Jumery

Termizi

et al. 2020

JTHEM

View full text Add to dashboard Cite

Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

show abstract

“…As originally proposed by Hyndman and Koehler (2006), the MASE is an internal measure as the comparison is made with performance of the na€ ıve benchmark during the fit period (Clements and Hendry 2005). However, some authors, Huddleston, Porter, and Brown (2015) for example, have adapted it to an external measure by choosing to scale by the performance of the na€ ıve benchmark during the forecast period instead of during the training period.…”

Section: Relmae5mae=mae Benchmarkmentioning

confidence: 99%

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Williams

Haworth

Blangiardo

et al. 2018

Geographical Analysis

View full text Add to dashboard Cite

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one-step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time-series forecasting methods like na€ ıve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London-wide, Borough, and neighborhood level.

show abstract

Improving forecasts for noisy geographic time series

Cited by 15 publications

References 21 publications

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

Forecasting International Tourist Arrivals in Malaysia Using Sarima and Holt-Winters Model

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Contact Info

Product

Resources

About