variability only to a long term increasingly upward trend. To isolate and properly investigates the seasonal component and possible relationships with the sources of variation in skin cancer incidence it is necessary to separate uncorrelated obscuring time scales such as random noise and the long term trend [7]. Separation is achieved using a combination of low pass Kolmogorov-Zurbenko Filters ...

The decomposition of time series into components is an important task that helps to understand and can enable better forecasting. Nowadays, with high sampling rates leading high-frequency data (such as daily, hourly, or minutely data), many real-world datasets contain that can exhibit multiple seasonal patterns. Although several methods have been proposed to decompose under these circumstances,...

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend e...

In this paper, we provide theoretical justification for the application of higher degree fuzzy transform in time series analysis. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and a random noise, we demonstrate that the higher degree fuzzy transform technique can be used for the estimation of the trend-cycle, which is one of the ba...

background there are few quantitative documents about the seasonal incidence of tuberculosis (tb) among immigrant populations. concerning the significant role of recognizing seasonal changes of tb in improving the tb control program, this study determines the trend and seasonal temporal changes of tb among immigrants entering iran.   methods in this longitudinal study, data from the iranian tb ...

Linear filters used in seasonal adjustment (model-based or from the X-11 method) contain unit root factors in the form of differencing operators and seasonal summation operators. The extent to which the various filters (seasonal, seasonal adjustment, trend, and irregular) contain these unit root factors determines whether the filters reproduce or annihilate (i) fixed seasonal effects, and (ii) ...

BACKGROUND The mortality rates of older people changes with the seasons. However, it has not been properly investigated whether the seasons affect medical care expenditure (MCE) and institutionalization. Seasonal variation in MCE is plausible, as MCE rises exponentially before death. It is therefore important to investigate the impact of the seasons on MCE both mediated and unmediated by mortal...

abstract climate variability empowers critical consequences on sustainability of soil and water resources. in this paper the trend of annual and seasonal time scale of temperature (minimum, maximum, average), relative humidity (minimum, maximum, average), precipitation, wind speed, extreme events, cloudiness, reference evapotranspiration employing mann-kendall and least square errors were studi...

Periodic time series analysis refers to the modelling approach where important time series properties depend on the period of the year. The standard approach to time series modelling is to treat a time series as a stochastic process with seasonal fluctuations. In a periodic analysis seasonal variations are modelled using separate yearly time series for each season, which do not possess seasonal...