This paper tackles the problem of forecasting real-life crime. However, recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis four simple and machine-learning-based ensemble methods. Additionally, we propose five techniques that manage seasonal component series. Furthermore, used symmetric mean average percentage error ...

Research on forecasting methods of time series data has become one the hot spots. More and more are produced in various fields. It provides for research analysis method, promotes development research. Due to generation highly complex large-scale data, construction models brings greater challenges. The main challenges modeling high complexity low accuracy poor generalization ability prediction m...

In this paper, we discuss a modified quaternion interpolation method based on interpolations performed the logarithmic form. This builds prior work that demonstrated approach maintains C2 continuity for prescriptive rotation. However, develop and extend to descriptive interpolation, i.e., interpolating an arbitrary time series. To accomplish this, provide robust of taking logarithm series such ...

The covariance matrix is formulated in the framework of a linear multivariate ARCH process with long memory, where the natural cross product structure of the covariance is generalized by adding two linear terms with their respective parameter. The residuals of the linear ARCH process are computed using historical data and the (inverse square root of the) covariance matrix. Simple measure of qua...

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...

[1] The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtai...