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

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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Time-Series Analysis of Seasonal Changes of the O Inversion Polymorphism of Drosophila subobscura

Genetics ◽

10.1093/genetics/142.1.179 ◽

1996 ◽

Vol 142 (1) ◽

pp. 179-187 ◽

Cited By ~ 1

Author(s):

Francisco Rodríguez-Trelles ◽

Gonzalo Alvarez ◽

Carlos Zapata

Keyword(s):

Time Series ◽

Seasonal Variation ◽

Time Series Analysis ◽

Seasonal Changes ◽

Climatic Factors ◽

Extreme Temperature ◽

Drosophila Subobscura ◽

Short Term ◽

Series Analysis

We have studied seasonal variation (spring, early summer, last summer and autumn) of inversion polymorphisms of the O chromosome of Drosophila subobscura in a natural population over 15 years. The length of the study allowed us to investigate the temporal behavior (short-term seasonal changes and long-term directional trends) of the O arrangements by the powerful statistical method of time series analysis. It is shown that the O inversion polymorphisms varied on two different time scales: short-term seasonal changes repeated over the years superimposed on long-term directional trends. All the common arrangements (O3+4+7, OST, O3+4 and O3+4+8) showed significant cyclic seasonal changes, and all but one of these arrangements (O3+4+7) showed significant long-term trends. Moreover, the degree of seasonality was different for different arrangements. Thus, O3+4+7 and OST showed the highest seasonality, which accounted for ∼61 and 47% of their total variances, respectively. The seasonal changes in the frequencies of chromosome arrangements were significantly associated with the seasonal variation of the climate (temperature, rainfall, humidity and insolation). In particular, O3+4+7 and OST, the arrangements with the greatest seasonal component, showed the strongest association with all climatic factors investigated, especially to the seasonal changes of extreme temperature and humidity.

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

Electronics ◽

10.3390/electronics10101151 ◽

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.

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