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

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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Relation between Land Subsidence and Deep Groundwater Exploitation in Cangzhou City

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.864-867.2213 ◽

2013 ◽

Vol 864-867 ◽

pp. 2213-2217 ◽

Cited By ~ 1

Author(s):

Ju Yan Zhu ◽

Hai Peng Guo

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Land Subsidence ◽

Quantitative Relationship ◽

Quantitative Relation ◽

Short Term ◽

Deep Groundwater ◽

Groundwater Exploitation ◽

Series Analysis

Due to long-term excessive exploitation of groundwater, serious land subsidence has been caused in Cangzhou City, Hebei Province, China. With GIS spatial analysis method, this paper conducted an analysis of the quantitative relationship between deep groundwater exploitation and the land subsidence in this area. This quantitative relation was analyzed by using data of both long-term and short-term time series. The long-term time series analysis indicates that the land subsidence volume accounts for 57.6% of the amount of deep groundwater exploitation, indirectly showing the proportion of released water from compressibility of the aquifers and the aquitards in deep groundwater exploitation. Some factors such as hysteresis effects of subsidence may be ignored in the short-term time series analysis, thus the calculated ratio becomes significantly large. From perspective of water resources evaluation, the long-term time series analysis is better to analyze the relation between land subsidence and deep groundwater exploitation.

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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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Swedish nationwide time series analysis of influenza and suicide deaths from 1910 to 1978

BMJ Open ◽

10.1136/bmjopen-2021-049302 ◽

2021 ◽

Vol 11 (7) ◽

pp. e049302

Author(s):

Christian Rück ◽

David Mataix-Cols ◽

Kinda Malki ◽

Mats Adler ◽

Oskar Flygare ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

20Th Century ◽

Short Term ◽

Death Rates ◽

Men And Women ◽

Series Analysis ◽

Suicide Rates ◽

Relationship Of

ObjectivesThere is concern that the COVID-19 pandemic will be associated with an increase in suicides, but evidence supporting a link between pandemics and suicide is limited. Using data from the three influenza pandemics of the 20th century, we aimed to investigate whether an association exists between influenza deaths and suicide deaths.DesignTime series analysis.SettingSweden.ParticipantsDeaths from influenza and suicides extracted from the Statistical Yearbook of Sweden for 1910–1978, covering three pandemics (the Spanish influenza, the Asian influenza and the Hong Kong influenza).Main outcome measuresAnnual suicide rates in Sweden among the whole population, men and women. Non-linear autoregressive distributed lag models was implemented to explore if there is a short-term and/or long-term relationship of increases and decreases in influenza death rates with suicide rates during 1910–1978.ResultsBetween 1910 and 1978, there was no evidence of either short-term or long-term significant associations between influenza death rates and changes in suicides (β coefficients of 0.00002, p=0.931 and β=0.00103, p=0.764 for short-term relationship of increases and decreases in influenza death rates, respectively, with suicide rates, and β=−0.0002, p=0.998 and β=0.00211, p=0.962 for long-term relationship of increases and decreases in influenza death rates, respectively, with suicide rates). The same pattern emerged in separate analyses for men and women.ConclusionsWe found no evidence of short-term or long-term association between influenza death rates and suicide death rates across three 20th century pandemics.

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Time Series Analysis and Forecasting Techniques for Foreign Direct Investment

Malaysian Journal of Social Sciences and Humanities (MJSSH) ◽

10.47405/mjssh.v6i9.1011 ◽

2021 ◽

Vol 6 (9) ◽

pp. 391-397

Author(s):

Ummi Rohaizad Abdul Rahim ◽

Zahayu Md Yusof

Keyword(s):

Time Series ◽

Foreign Direct Investment ◽

Time Series Analysis ◽

Direct Investment ◽

Equity Capital ◽

Short Term ◽

Series Analysis ◽

Business Metrics ◽

Forecasting Techniques

Foreign direct investment are the net inflows of investment to acquire a lasting management interest which is 10 percent or more of voting stock in an enterprise operating in an economy other than the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This paper will discuss the definitions and findings of previous studies regarding Foreign Direct Investment. This paper also will explain about forecasting techniques used in previous studies in forecasting Foreign Direct Investment. Time Series Analysis is used to determine a good model that can be used to forecast business metrics.

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