Statistical Indices of Business Conditions

Copeland, Melvin T.

doi:10.2307/1885464

Cited by 12 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Later, his approach was extended by Hooker (1901), Spencer (1904) and Anderson and Nochmals (1914). Copeland (1915) was the first who attempted to extract the seasonal component, and Macaulay (1931) proposed a method which is currently considered "classical".…”

Section: Time Series Decompositionmentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

show abstract

Section: Time Series Decompositionmentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…It was in this way that such indices as Babson’s Composite Plot, first published in the United States before World War 1, came into being. At that time, discussions on this index’s value raised similar arguments for and against indices, in the same way as modern-day discussions’ of the value of composite indicators and scoreboards (Copeland 1915 ; McDowall 2008 ). The argument in favour of composite indicators was their conciseness.…”

Section: A Glut Of Indicesmentioning

confidence: 81%

Innovation indices: the need for positioning them where they properly belong

Kozłowski

2015

Scientometrics

View full text Add to dashboard Cite

A specific quality of the discussion about innovation indices (scoreboards) is that more often than not the subject is dealt with from a purely technical point of view. Such a narrow approach silently assumes that indices used as a policy tool are an accurate reflection of the phenomenon and should not be questioned, and also that the whole discussion concerning them should refer to methodological aspects and is best left to the statisticians. This author is of the opinion that for an accurate evaluation of the value of indices as a policy tool, it is necessary to consider the matter from the broader point of view and from the context in which such indices are generated and used. This article puts forward the thesis that progress in science and innovation policy studies depends on a diversity of issues, approaches and perspectives. If that is the case, maintaining thematic and methodological variety may be more important than creating coherent and closed analytical tools, i.e. indices. The advantage of indices is that they focus attention on those variables which are deemed to be key. Among their disadvantages, however, are their highly abstract nature (in order to understand innovation-related phenomena, it is necessary to study them in tangible, composite forms); their tendency to skip unmeasurable determinants; their prior acceptance of definitions and concepts of innovation (instead of searching for them); the way they apply a single yardstick to diverse countries and regions, assumed linearity and causality in a complex and non-linear world, the way they direct policy towards implementing indicators (rather than identifying and solving problems). It is suggested that big data revolution will allow the emergence of a new measurement tools that will replace innovation indices.

show abstract

“…Persons indicated that time series was constituted of four types of fluctuations (Dagum and Bianconcini, 2016): residual variations, seasonal movement, secular trend, and cyclical movements. Further, research in that direction included Copeland (1915) and Persons (1919), who introduced for extracting the seasonal component until Macaulay (1931) suggested a technique which turned into "classical" in the log run. Macaulay (1931) developed a computer program which is significantly simplified the calculations (Shiskin, 1957).…”

Section: Seasonal Decompositionmentioning

confidence: 99%

Automatic Time Series Forecasting with Ata Method in R: ATAforecasting Package

Ali¹,

Yapar²,

Hanife³

2021

The R Journal

View full text Add to dashboard Cite

Ata method is a new univariate time series forecasting method that provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. The Ata method's forecasting performance is superior to existing methods in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or deseasonalized time series, where the deseasonalization can be performed via any preferred decomposition method. The R package ATAforecasting was developed as a comprehensive toolkit for automatic time series forecasting. It focuses on modeling all types of time series components with any preferred Ata methods and handling seasonality patterns by utilizing some popular decomposition techniques. The ATAforecasting package allows researchers to model seasonality with STL, STLplus, TBATS, stR, and TRAMO/SEATS, and power family transformation and analyze the any time series with a simple Ata method and additive, multiplicative, damped trend the Ata methods and level fixed Ata trended methods. It offers functions for researchers and data analysts to model any type of time series data sets without requiring specialization. However, an expert user may use the functions that can model all possible time series behaviors. The package also incorporates types of model specifications and their graphs, uses different accuracy measures that surely increase the Ata method's performance.

show abstract

Statistical Indices of Business Conditions

Cited by 12 publications

References 0 publications

Forecasting: theory and practice

Forecasting: theory and practice

Innovation indices: the need for positioning them where they properly belong

Automatic Time Series Forecasting with Ata Method in R: ATAforecasting Package

Contact Info

Product

Resources

About