scholarly journals Time Series Analysis of Decadal Precipitation Pattern at Selected Cities of Southern India

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
T. S. Subbiah ◽  
P. Parthiban ◽  
R. Mahesh ◽  
A. Das
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Stochastic Modeling ◽  
Tamil Nadu ◽  
Exponential Smoothing ◽  
Southern India ◽  
Precipitation Pattern ◽  
Short Term ◽  
Series Analysis

To characterize and explore the short-term climatic patterns over the last decade (Jan. 2009 to Dec. 2018), the present research has been carried out, involving time series analysis of precipitation pattern in three cities of Tamil Nadu, namely, Thanjavur, Nagapattinam, and Chennai, referring to deltaic, coastal and highly urbanized cities of Tamil Nadu, respectively. The study involves time series empirical analysis, decomposition, exponential smoothing, and various stochastic modeling. Herein, the location-specific suitable models are obtained and specific predictions are being carried out, as well.

scholarly journals Forecasting the spread of COVID19 in Hungary

2020 ◽  
Author(s):  
Owais Mujtaba Khanday ◽  
Samad Dadvandipour ◽  
Mohd. Aaqib Lone
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Reproduction Number ◽  
Sir Model ◽  
Exponential Smoothing ◽  
Short Term ◽  
Smoothing Methods ◽  
Series Analysis ◽  
Short Term Forecasting

AbstractTime series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.

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.

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