Analysis of attention on venture capital: A method of complex network on time series

Venture capital is an important force in promoting technological innovation and social progress. Research regarding the attention on venture capital can help understand the development and social influence of venture capital, which is conducive to the policy guidance and incentive to the industry. With the rapid development of information technology, the Internet has become the main source and dissemination channel of information, and search engines have become an important interface for information. Through a deep analysis of the search index of venture capital, we can find information that is more important. Based on the principle of time series visualization, we have transformed the time series data of attention on venture capital into complex networks and analyzed its network characteristics. We collected the Baidu index of Chinese venture capital from January 1, 2018 to November 25, 2019 and constructed a time series network based on PC plus mobile search, PC search, and mobile search. The results show that the convenience of the mobile terminal offers makes it the primary mode of tracking the industrial dynamics of venture capital, especially hot news. Relatively, PC terminal search is more stable than mobile search, more focused on industry reports, and refers to long-term followers of venture capital. Both degree distribution and centrality distribution of the three networks show that abnormal peaks and lows are few and the number of key time points in the time series of search on venture capital is insignificant. However, the clustering results show obvious segmentation effect that the peak effect of hot news is evident.

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An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093321 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3321

Author(s):

Shaolong Zeng ◽

Yiqun Liu ◽

Junjie Ding ◽

Danlu Xu

Keyword(s):

Time Series ◽

Economic Development ◽

Energy Consumption ◽

Open Economy ◽

High Speed ◽

Time Series Data ◽

Rapid Development ◽

Series Data ◽

High Quality ◽

The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

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Data mining model for multimedia financial time series using information entropy

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189019 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5339-5345

Author(s):

Han He ◽

Yuanyuan Hong ◽

Weiwei Liu ◽

Sung-A Kim

Keyword(s):

Data Mining ◽

Time Series ◽

Time Series Data ◽

Financial Time Series ◽

Rapid Development ◽

Development Trend ◽

Series Data ◽

Transaction Data ◽

Relationship Analysis ◽

Financial Time

At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

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A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

10.36227/techrxiv.16640212.v1 ◽

2021 ◽

Author(s):

Jaydip Sen

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Real Time ◽

Stock Prices ◽

Time Series Data ◽

Rapid Development ◽

Series Data ◽

Time Processing ◽

Forecasting Models ◽

Series Analysis

<p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models. </p>

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A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

10.36227/techrxiv.16640212 ◽

2021 ◽

Author(s):

Jaydip Sen

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Real Time ◽

Stock Prices ◽

Time Series Data ◽

Rapid Development ◽

Series Data ◽

Time Processing ◽

Forecasting Models ◽

Series Analysis

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Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review

Systematic Reviews ◽

10.1186/s13643-020-01515-2 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Tripti Rastogi ◽

Anne Backes ◽

Susanne Schmitz ◽

Guy Fagherazzi ◽

Vincent van Hees ◽

...

Keyword(s):

Physical Activity ◽

Time Series ◽

Scoping Review ◽

Analytical Methods ◽

Time Series Data ◽

Rapid Development ◽

Series Data ◽

Future Research ◽

Accelerometer Data ◽

Analytical Approaches

Abstract Background Physical activity (PA) is a complex multidimensional human behaviour. Currently, there is no standardised approach for measuring PA using wearable accelerometers in health research. The total volume of PA is an important variable because it includes the frequency, intensity and duration of activity bouts, but it reduces them down to a single summary variable. Therefore, analytical approaches using accelerometer raw time series data taking into account the way PA are accumulated over time may provide more clinically relevant features of physical behaviour. Advances on these fields are highly needed in the context of the rapid development of digital health studies using connected trackers and smartwatches. The objective of this review will be to map advanced analytical approaches and their multidimensional summary variables used to provide a comprehensive picture of PA behaviour. Methods This scoping review will be guided by the Arksey and O’Malley methodological framework. A search for relevant publications will be undertaken in MEDLINE (PubMed), Embase and Web of Science databases. The selection of articles will be limited to studies published in English from January 2010 onwards. Studies including analytical methods that go beyond total PA volume, average daily acceleration and the conventional cut-point approaches, involving tri-axial accelerometer data will be included. Two reviewers will independently screen all citations, full-text articles and extract data. The data will be collated, stored and charted to provide a descriptive summary of the analytical methods and outputs, their strengths and limitations and their association with different health outcomes. Discussion This protocol describes a systematic method to identify, map and synthesise advanced analytical approaches and their multidimensional summary variables used to investigate PA behaviour and identify potentially clinically relevant features. The results of this review will be useful to guide future research related to analysing PA patterns, investigate their association with health conditions and suggest appropriate recommendations for changes in PA behaviour. The results may be of interest to sports scientists, clinical researchers, epidemiologists and smartphone application developers in the field of PA assessment. Scoping review registration This protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb.

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Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142050010x ◽

2019 ◽

Vol 34 (05) ◽

pp. 2050010 ◽

Cited By ~ 1

Author(s):

Peng Zhan ◽

Changchang Sun ◽

Yupeng Hu ◽

Wei Luo ◽

Jiecai Zheng ◽

...

Keyword(s):

Time Series ◽

Big Data ◽

Similarity Search ◽

High Speed ◽

High Performance ◽

Time Series Data ◽

Rapid Development ◽

Feature Representation ◽

Series Data ◽

Data Points

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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