scholarly journals Time series analysis of radio signal WET tropospheric delays for short-term forecast

2015 ◽  
pp. 345-354 ◽  
Author(s):  
Zofia Rzepecka
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Radio Signal ◽  
Short Term ◽  
Short Term Forecast ◽  
Series Analysis ◽  
Term Forecast ◽  
Tropospheric Delays

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

The effects of temperature on short-term mortality risk in Kuwait: A time-series analysis

2019 ◽  
Vol 171 ◽  
pp. 278-284 ◽  
Author(s):  
Barrak Alahmad ◽  
Ahmed Shakarchi ◽  
Mohammad Alseaidan ◽  
Mary Fox
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Mortality Risk ◽  
Short Term ◽  
Series Analysis ◽  
Short Term Mortality ◽  
Effects Of Temperature

ANALYSIS OF TIME SERIES WITH WAVELETS

2007 ◽  
Vol 05 (02) ◽  
pp. 241-256 ◽  
Author(s):  
ARMANDO CIANCIO
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Financial Time Series ◽  
Wavelet Coefficients ◽  
Analysis Method ◽  
Short Term ◽  
Financial Time ◽  
Local Characteristics ◽  
Series Analysis ◽  
Analysis Of Time Series

A financial time series analysis method based on the theory of wavelets is proposed. It is based on the transformation of data of the series in the corresponding wavelet coefficients and in the analysis of the latter, which represent the local characteristics of the series better. In particular, an algorithm for short term previsions is defined.

The effect of season and the introduction of rams on oestrous activity in Somali, Nandi, Merino, Karakul and New Zealand Romney Marsh ewes in Kenya

1986 ◽  
Vol 43 (3) ◽  
pp. 447-457 ◽  
Author(s):  
A. B. Carles ◽  
W. A. K. Kipngeno
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
New Zealand ◽  
Time Series Analysis ◽  
Oestrous Cycle ◽  
Significant Source ◽  
Short Term ◽  
Series Analysis ◽  
The Mean

ABSTRACTA study was made of the levels of oestrous activity of two indigenous breeds of sheep (Somali and Nandi) and three exotic breeds of sheep (Merino, Karakul and New Zealand Romney Marsh) over a period of 3 years, in an equatorial environment. Breed was the only significant source of variation for the length of the oestrous cycle (P < 0·01). The mean lengths of the oestrous cycle were 17·2 (s.d. 3·21), 17·5 (s.d. 2·24), 17·9 (s.d. 2·99), 17·5 (s.d. 2·57) and 16·5 (s.d. 3·41) days for the Somali, Nandi, Merino, Karakul and Romney Marsh breeds, respectively.The mean percentage of ewes of the different breeds showing oestrus in 20-day periods were 69·8 (s.d. 22·57), 49·9 (s.d. 18·67), 63·4 (s.d. 25·70), 79·2 (s.d. 20·30) and 33·2 (s.d. 23·50) % for the Somali, Nandi, Merino, Karakul and Romney Marsh breeds, respectively. Time-series analysis did not detect any evidence of seasonal variation in oestrous activity, although there was an indication that the Merino and Romney Marsh breeds showed a marked increase in oestrous activity following, the introduction of rams. It was concluded that the variation in level of oestrous activity was short term and random.

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