Sentences with phrase «do time series analysis»

That said, maybe the tuning is a good way to transform from depth to time, but then to do time series analysis on the result and claim anything about the frequencies associated with the tuning target seems misguided to say the least.

Last I heard, Tamino claims to have an unrelated day job doing time series analysis, whatever that means.

Not exact matches

Lacking a mechanistic explanation for this time - series analysis - derived cycle, we don't know — in fact, all the PDO is (so far) is a statistical phenomenon.

An archival time - series analysis is simple enough that readers do not need a background in statistics to understand the underlying logic.

A time - series analysis just might do the trick!

Is the time series analysis consistent, i.e. does it take account of academy conversions?

We do not want our analysis to rest heavily on synthesised data, but we consider one very long time series: that of the Dow Jones Industrial Average (DJIA).

Thompson et al. do not provide a time series estimate on the effects of the bias on the global temperature record, but Steve McIntyre, who is building an impressive track record of analyses outside the peer - review system, discussed this topic on his weblog

I think that doing this on the basis of time series analysis is somewhere between hard and impossible.

This is a grossly imperfect way to do this, of course, but a reasonable approximation to the real time - series analysis.

If it matters at all, I never found a use for FFT in my previous line of work (population ecology), although I did have occasion to use PCA on a time series analysis.

Discounting Tamino's analysis because you don't like it or don't consider him objective doesn't detract from the fact the Tamino is a professional time - series analyst whose work in climate science not only stands the test of time but is widely considered a de facto standard in climate science.

@DS: This is why you do not smooth a time series then use the smoothed data as input into a subsequent analysis.

Second why not hold out 50 % of your temp time series (random selection of each data point perhaps), do your analysis on on one half and check the fit to the other.

Of course, the statistical analysis I've presented here is very crude, and there are lots of better things you can do with more data and fancier time - series techniques.

It is worth noting that even under Dr K's analysis of 1 / f noise, that 1 / f noise time series does indeed have a population mean, and it is quite relevant to compute forcings of things like CO2 doubling relative to the population mean.

The misstatement that they are not is evidence that somebody did not pay attention in the time series analysis cours they should have taken prior to being called an expert in climate science

This doesn't affect your results, but I think you have your terminology switched around: The term «eof» refers to the loading (which is usually a spatial pattern in these analyses), and «pc» can mean either the time - series or the combination of time - series + eof.

Personally, I would not do straight time - series analysis of temperature data in the current era unless it contained a larger dynamic range.

for lack of warming since 1998» refers to a model that does address serial correlation (being based on Kaufman, A., H. Kauppi, and J. H. Stock, 2006: «Emissions, concentrations and temperature: a time series analysis.»

Nicola Scafetta says «It seems to me that you are arguing that because a clear physical mechanism is still missing and the calculations were done on a short time series than the result of our analysis should be «rejected».

It seems to me that you are arguing that because a clear physical mechanism is still missing and the calculations were done on a short time series than the result of our analysis should be «rejected».

Analyses of the annual mean streamflow time series for the 14 streamflow clusters indicated periods of extended wet and dry periods, but did not indicate any strong monotonic trends.

Improved time series analysis methods do not support the statistical significance and likelihood levels of the IPCC's conclusion that sea level rise has accelerated in the 20th century relative to the 19th century.

For example, the proxies in Gergis were screened against correlations with other grid cells within 500 Km (a rationale for why 500, and not say 477, or 567 km was appropriate... we aren't told how many of the the time series correlated with adjacent grid cells, and how often the included or excluded times series DID NT correlate with adjacent grid cells... This kind of stuff is reported and considered when conducting an exploratory analysis.

However, time - series analyses did not reveal any relationship between the proportion of male stillbirths and mean annual ambient temperature.

In short, the global temperature time series clearly does not follow the model adopted in Schwartz's analysis.

Fraedrich & Blender find persistence up to decades, Kiraly et al. find persistence lasting several years, so even if their analysis applied to temperature time series (which it doesn't) rather than fluctuations (which it does), those time scales aren't long enough to explain the trend on a century time scale in observed temperature time series.

Rather than looking at the dataset available in 1998 and using standard time series analysis techniques (available in 1998) to establish its underlying structure and hence the null hypothesis, the choice in null hypothesis was limited to a couple of options (trend vs no - trend) that were particularly naive (probably more naive than human instinct) and a selection process was established that appears to use the skill criteria (exactly what was done is unclear from the text) and inappropriate multiple use of the dataset.

Nonstationarity of error terms is a serious problem in time - series analysis, but I don't have a good sense of how well this issue has been treated in climate analysis.

The posts did point out that «Some of the other pieces in this series are fine» but do not reflect the large amount of analysis in the investigation of the way the emails have been misused by those with a political agenda and the extensive context we included to indicate the pressure scientists writing those emails were under from time - consuming requests for data.

Also, for time series analyse4s, where it would be useful, say, to update a published analysis with the latest few years of data, this is already normally done via a database on on the authors website...

I have a feeling a lot of PhDs could get minted from extending your analysis here alone ---- does it hold for longer time series, using Vostok Ice Cores (not tree rings please!!)

b) then start on the CO2 forcing / CO2 concentration time series analysis which would similar process as just done of the analysis for GISS time series

What I don't understand is why there is so much angst about what is after all only simple empirical observations about the nature of a time series (even if aspects of the analysis maybe open to theoretical debate), and so little curiosity about what this all means for statistical inference more generally in climate science.

If I understood VS correctly (don't be too harsh on me if not) there's a way to detect such behaviour in data by applying some time series analysis procedures.