Clustering Based on Forecasting Density:Case Study of Unemployment Rate in Iran's Provinces

It is important for regional planners and policymakers to be aware of the unemployment rate of the provinces in specified time horizons. In this paper, clustering of time series based on their forecasting density to a specified horizon is investigated. In this algorithm, we use the bootstrap process to approximate the distribution of predictions. The differences between each pair of bootstrap densities generate a dissimilarity matrix that is used for clustering. For this purpose, seasonal unemployment data was used in the spring of 2005 to fall of 2017, and according to the forecasting density algorithm, we will cluster the unemployment rate of Iran's provinces for two horizons of 4 steps (one year) and 10 steps (two and a half years). The best situation will be in the 4 steps or 10 steps (two and a half years), the provinces of Semnan and Zanjan, and the worst situation in the provinces of Lorestan and Kermanshah. Also, in the two horizons studied, except for some provinces, the rest were fixed in their main clusters. The spatial distribution of unemployment in Iran, based on forecasting density clustering, shows that western and southwestern provinces will have the highest unemployment rates. Therefore, the need for regional planning and serious attention to the employment of these provinces is recommended. At the same time, the provinces that are in an unfavorable situation have high unemployment neighbors, and the provinces with low unemployment rate have predominantly neighborhoods with a low unemployment rate. In other words, there is a positive spatial correlation between the neighboring provinces and the unemployment rate.