Example of Journal of Time Series Analysis format
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Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format
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Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format Example of Journal of Time Series Analysis format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Journal of Time Series Analysis — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #59 of 152 down down by 1 rank
Statistics and Probability #97 of 239 down down by 1 rank
Applied Mathematics #268 of 548 down down by 41 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 186 Published Papers | 358 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/06/2020
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Related Journals

open access Open Access

Taylor and Francis

Quality:  
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CiteRatio: 1.9
SJR: 0.626
SNIP: 1.193
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Quality:  
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CiteRatio: 1.4
SJR: 0.622
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Taylor and Francis

Quality:  
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open access Open Access

Springer

Quality:  
High
CiteRatio: 2.9
SJR: 1.151
SNIP: 1.392

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

0.817

10% from 2018

Impact factor for Journal of Time Series Analysis from 2016 - 2019
Year Value
2019 0.817
2018 0.906
2017 0.826
2016 0.975
graph view Graph view
table view Table view

1.9

19% from 2019

CiteRatio for Journal of Time Series Analysis from 2016 - 2020
Year Value
2020 1.9
2019 1.6
2018 1.6
2017 1.6
2016 1.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 10% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 19% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.576

34% from 2019

SJR for Journal of Time Series Analysis from 2016 - 2020
Year Value
2020 1.576
2019 1.173
2018 1.189
2017 1.395
2016 1.185
graph view Graph view
table view Table view

1.586

24% from 2019

SNIP for Journal of Time Series Analysis from 2016 - 2020
Year Value
2020 1.586
2019 1.281
2018 1.138
2017 1.342
2016 1.318
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 34% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 24% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Time Series Analysis

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Wiley

Journal of Time Series Analysis

During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of bi...... Read More

Statistics, Probability and Uncertainty

Statistics and Probability

Applied Mathematics

Decision Sciences

i
Last updated on
16 Jun 2020
i
ISSN
0143-9782
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Impact Factor
High - 1.197
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. 1982. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

Journal Article DOI: 10.1111/J.1467-9892.1980.TB00297.X
An introduction to long‐memory time series models and fractional differencing
Clive W. J. Granger1, Roselyne Joyeux1

Abstract:

. The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1-B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forec... . The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1-B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown to possibly arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented. read more read less

Topics:

Autoregressive fractionally integrated moving average (56%)56% related to the paper, Filter (signal processing) (53%)53% related to the paper, White noise (53%)53% related to the paper, Series (mathematics) (51%)51% related to the paper
3,250 Citations
Journal Article DOI: 10.1111/J.1467-9892.1983.TB00371.X
The estimation and application of long memory time series models
John Geweke1, Susan Porter-Hudak1

Abstract:

. The definitions of fractional Gaussian noise and integrated (or fractionally differenced) series are generalized, and it is shown that the two concepts are equivalent. A new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic reg... . The definitions of fractional Gaussian noise and integrated (or fractionally differenced) series are generalized, and it is shown that the two concepts are equivalent. A new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic regressor. The estimator is the ordinary least squares estimator of the slope parameter in this regression, formed using only the lowest frequency ordinates of the log periodogram. Its asymptotic distribution is derived, from which it is evident that the conventional interpretation of these least squares statistics is justified in large samples. Using synthetic data the asymptotic theory proves to be reliable in samples of 50 observations or more. For three postwar monthly economic time series, the estimated integrated series model provides more reliable out-of-sample forecasts than do more conventional procedures. read more read less

Topics:

Ordinary least squares (60%)60% related to the paper, Estimator (59%)59% related to the paper, Least squares (59%)59% related to the paper, Autoregressive fractionally integrated moving average (58%)58% related to the paper, Simple linear regression (57%)57% related to the paper
3,070 Citations
open accessOpen access Journal Article DOI: 10.1111/1467-9892.00091
Error‐correction Mechanism Tests for Cointegration in a Single‐equation Framework
Anindya Banerjee1, Juan J. Dolado1, Ricardo Mestre2

Abstract:

A new test is proposed for cointegration in a single-equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as an error-correction mechanism (ECM) test and is based upon the ordinary least squares coefficient of the lagged dependent variable in an autoregressive distri... A new test is proposed for cointegration in a single-equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as an error-correction mechanism (ECM) test and is based upon the ordinary least squares coefficient of the lagged dependent variable in an autoregressive distributed lag model augmented with leads of the regressors. The limit distributions of the standardized coeffi cient and t-ratio versions of the ECM tests are obtained and critical values are provided. These limit distributions do not depend upon nuisance parameters but they depend on the number of regressors. Finally, we compare their power properties with those of other cointegration tests available in the literature and find the circumstances under which the ECM tests have a better performance. read more read less

Topics:

Asymmetric cointegration (55%)55% related to the paper, Distributed lag (52%)52% related to the paper, Autoregressive model (51%)51% related to the paper, Cointegration (51%)51% related to the paper
View PDF
1,952 Citations
Journal Article DOI: 10.1111/J.1467-9892.1982.TB00349.X
An approach to time series smoothing and forecasting using the em algorithm
Robert H. Shumway1, David S. Stoffer2

Abstract:

. An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state-space model, the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters by maximum likelihood. An example ... . An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state-space model, the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters by maximum likelihood. An example is given which involves smoothing and forecasting an economic series using the maximum likelihood estimators for the parameters. read more read less

Topics:

Smoothing (61%)61% related to the paper, Expectation–maximization algorithm (60%)60% related to the paper, Estimator (54%)54% related to the paper, Kalman filter (53%)53% related to the paper, Missing data (51%)51% related to the paper
1,513 Citations
Journal Article DOI: 10.1111/J.1467-9892.1983.TB00373.X
Diagnostic checking arma time series models using squared‐residual autocorrelations
A. I. McLeod1, Wai Keung Li2

Abstract:

. Squared-residual autocorrelations have been found useful in detecting nonlinear types of statistical dependence in the residuals of fitted autoregressive-moving average (ARMA) models (Granger and Andersen, 1978; Miller, 1979). In this note it is shown that the normalized squared-residual autocorrelations are asymptotically ... . Squared-residual autocorrelations have been found useful in detecting nonlinear types of statistical dependence in the residuals of fitted autoregressive-moving average (ARMA) models (Granger and Andersen, 1978; Miller, 1979). In this note it is shown that the normalized squared-residual autocorrelations are asymptotically unit multivariate normal. The results of a simulation experiment confirming the small-sample validity of the proposed tests is reported. read more read less

Topics:

Ljung–Box test (59%)59% related to the paper, Portmanteau test (54%)54% related to the paper
1,135 Citations
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Frequently asked questions

1. Can I write Journal of Time Series Analysis in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Time Series Analysis guidelines and auto format it.

2. Do you follow the Journal of Time Series Analysis guidelines?

Yes, the template is compliant with the Journal of Time Series Analysis guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Time Series Analysis?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Time Series Analysis citation style.

4. Can I use the Journal of Time Series Analysis templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Time Series Analysis.

5. Can I use a manuscript in Journal of Time Series Analysis that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Time Series Analysis that you can download at the end.

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7. Where can I find the template for the Journal of Time Series Analysis?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Time Series Analysis's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of Time Series Analysis's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's Journal of Time Series Analysis is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Time Series Analysis?

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11. What is the output that I would get after using Journal of Time Series Analysis?

After writing your paper autoformatting in Journal of Time Series Analysis, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Time Series Analysis's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Time Series Analysis?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Time Series Analysis. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Time Series Analysis?

The 5 most common citation types in order of usage for Journal of Time Series Analysis are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Time Series Analysis?

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16. Can I download Journal of Time Series Analysis in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Time Series Analysis Endnote style according to Elsevier guidelines.

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