Nature and Composition of Services Sector in India – A Time Series Analysis

Indian Journal of Economics and Development ◽

10.35716/ijed/20093 ◽

2021 ◽

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Gross Domestic Product ◽

Good Governance ◽

High Growth ◽

Domestic Product ◽

Services Sector ◽

Indian Economy ◽

Series Analysis

The services sector is the backbone of the Indian economy, accounting for the majority of the country's gross domestic product (GDP).It contributes towards the development and growth of the overall economy to a great extent. Findings of the present study revealed that in India, there existed a long-term relationship between services and GDP.From1950-70 CAGR of GDP was found stable whereas it faced a declining trend in 1970-80 but the period of 2000-2013 witnessed a high growth. Despite being one of the world's fastest-growing economies, India still needs to reform itself more in order to attain high growth and long-term sustainability goals. There is an urgent need for good governance to remove the constraints and hindrances for developing services in India.

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Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China

Scientific Reports ◽

10.1038/s41598-016-0020-5 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 10

Author(s):

Tao Zhang ◽

Fei Yin ◽

Ting Zhou ◽

Xing-Yu Zhang ◽

Xiao-Song Li

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Gross Domestic Product ◽

Multivariate Time Series ◽

Domestic Product ◽

Notifiable Diseases ◽

Series Analysis ◽

Dynamic Relationship ◽

Multivariate Time Series Analysis ◽

Class B

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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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