A survey on forecasting of time series data

Mahalakshmi, G. S.; Sridevi, S.; Rajaram, S.

doi:10.1109/icctide.2016.7725358

Cited by 109 publications

(59 citation statements)

References 42 publications

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“…For a total of N given historical data in the dataset, p x and the corresponding forecasted or predicted value p̂x , such that x = 1 to N, the performance evaluation factors are given as [20],…”

Section: B Evaluation Of Prediction Methodsmentioning

confidence: 99%

Survey: Sentiment Analysis of Twitter Data for Stock Market Prediction

Jadhav¹,

Wakode²

2017

International Journal of Advanced Research in Computer and Comm

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Stock market prediction is trying to determine the future value of a company stock. The company should make a profit when the prediction of stock"s future price could be successfully done. News article and social media can affect the stock market. The Twitter is one of the most useful social media that can help to predict public mood. The predicted mood and previous day value are useful for predicting stock market price. This paper discusses the prediction of the future stock market with help of sentiment score. The different techniques can be used to classify result of sentiment score. In this paper, the different technique for stock market prediction is introduced. We also discuss technology that speedup the computation which will improve the performance.

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Section: B Evaluation Of Prediction Methodsmentioning

confidence: 99%

Survey: Sentiment Analysis of Twitter Data for Stock Market Prediction

Jadhav¹,

Wakode²

2017

International Journal of Advanced Research in Computer and Comm

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“…Data mining [67,114,183], text analytics [3] and video analytics [117] each are well-described in the referenced authoritative texts. Timeseries forecasting [123], analysis and control [29] have also been reviewed in detail. Literature also covers business analytics processes [111], prescriptive analytics [22] and techniques [193].…”

Section: A Layered Taxonomy Of Data Analytics and Applications For Tmentioning

confidence: 99%

Analytics for the Internet of Things

2018

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The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.

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“…With the time series, then the data movement pattern or variable values can be followed or known. For several years various types of research on the prediction of goods with time series analysis have been developed [5,12,13], several methods using autoregressive (AR), moving average (MA), and merging the two models. The Autoregressive Integrated Moving Average (ARIMA) is a development method of autoregressive (AR), integrated (I), and moving average (MA) [14].…”

Section: Forecasting Time Series Modelmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Verano

Husnawati

Ermatita

2020

JITeCS

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The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

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A survey on forecasting of time series data

Cited by 109 publications

References 42 publications

Survey: Sentiment Analysis of Twitter Data for Stock Market Prediction

Survey: Sentiment Analysis of Twitter Data for Stock Market Prediction

Analytics for the Internet of Things

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

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