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Books on the topic 'Forecasting of data in the form of time series'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

A, Dickey David, ed. SAS for forecasting time series. 2nd ed. New York: J. Wiley, 2003.

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2

Brocklebank, John Clare. SAS system for forecasting time series. Cary, NC: SAS Institute, 1986.

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3

Maiti, Raju. Modelling and coherent forecasting of zero-inflated time series count data. Ahmedabad: Indian Institute of Management, 2013.

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4

Riazuddin, Riaz. Detection and forecasting of Islamic calendar effects in Time Series Data. Karachi: State Bank of Pakistan, 2002.

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5

Liu, Lon-Mu. Forecasting and time series analysis using the SCA statistical system. River Forest, Ill: Scientific Computing Associates, 2004.

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6

Bastian, Jörgen. Optimale Zeitreihenprognose: Empirische Probleme und Lösungen. Frankfurt am Main: P. Lang, 1985.

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7

Bagus, Erich. Computergestützte Zeitreihenprognose mit linear-rekursiven Modellen. Idstein: Schulz-Kirchner Verlag, 1994.

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8

Intelligent systems and financial forecasting. London: Springer, 1997.

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9

Dickey, David A., and John C. Brocklebank. SAS for Forecasting Time Series. WA (Wiley-SAS), 2003.

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10

Agung, I. Gusti Ngurah. Advanced Time Series Data Analysis: Forecasting Using EViews. Wiley & Sons, Incorporated, John, 2018.

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11

Dickey, David A., and John C., Ph.D. Brocklebank. SAS for Forecasting Time Series, Second Edition. 2nd ed. SAS Publishing, 2003.

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12

SAS System for Forecasting Time Series, 1986 Edition. 8th ed. SAS Institute, Incorporated, 1986.

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13

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control). Springer, 2005.

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14

Valeriano, Brandon. The Correlates of Cyber Strategy. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190618094.003.0003.

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This chapter explores the effects of cyber strategy and coercion through an empirical lens. It addresses this empirical gap and conducts a series of quantitative tests to answer key questions based on data collected covering the years 2000–2014. The results of the data analyses suggest cyber operations rarely produce concessions. The digital domain demonstrates minimal coercive utility to date. A state’s latent cyber capacity, as a proxy measure of potential cyber power in a state, is not a significant predictor of coercive potential. The analysis demonstrates more traditional arbiters of strategic competition such as military or economic power are likely better predictors for explaining rival behavior. Finally, it examines escalation in cyber incidents, which lead to some form of escalation 53.65% of the time. When the Russia-Georgia and Russia-Ukraine dyads are removed, all military escalation involved only threats and displays of force. Escalation observed after cyber incidents tends to be limited and consistent with rivalry dynamics.

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15

Makse, Todd, Scott Minkoff, and Anand Sokhey. Politics on Display. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190926311.001.0001.

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Political yard signs are one of the most conspicuous features of American political campaigns, yet they have received little attention as a form of political communication or participation. In a climate in which the American public is highly polarized, these symbols are more than simple campaign tools—they are literal markers of partisan identity. As public cues that push into private life, they affect individuals and their neighborhoods, coloring perceptions of social spaces and impacting social networks. In Politics on Display we answer a series of questions about this familiar feature of electoral politics: Why do people put their preferences out there for the world to see? Do neighborhoods become political battlegrounds? And what are the consequences of displaying yard signs in these spaces where we spend most of our time? We answer these questions with an innovative research design, documenting political life in neighborhoods with complementary data sources: street-level observation of the placement of signs and neighborhood-specific survey research that delves into the attitudes, behavior, and social networks of residents. Integrating these data into a geo-database that also includes demographic and election data—and supplementing these data with nationally representative studies—we bring together insights from political communication, political psychology, and political geography. Against a backdrop of today’s political environment of conflict and division, we advance a new understanding of how citizens experience campaigns, why many still insist on airing their views in public, and what happens when social spaces become political spaces.

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