Highstocks give great tools for data analysis with it stock tools indicators
Is it anyhow possilbe to show eg the standard deviation (R-squared) for trend line?
Stock Tools Documentation
Technical Indicators Overview see eg linear regression sample linke on this page
Thx, I really appreciate your help and expertise!
Related
I'm trying to analyze graphs about social media. The graphs contains time information, so it's possible to do some time series analysis. For each time point, I can run a community detection algorithm (e.g. Louvain method) to detect communities at that time. I can see that the communities are evolving: nodes in smaller communities are sometimes merging into a bigger community, and sometimes they are splitting up. However, I failed to find a comprehensive visualization tool to analyze and demonstrate the evolution of the communities.
Does anyone recommend a tool to serve this purpose? Thank you.
There is requirement where we have to calculate the throughput for the agile train teams. Is there any chart in JIRA using which we can calculate throughput for agile train teams.
Probably the closest thing to this is the control chart.
It will show you the rolling average of the mean time it takes for items to pass through your team's workflow.
This script might be really useful for you: http://www.littlebluemonkey.com/blog/automatically-import-jira-backlog-into-google-spreadsheet
It's a script that connects a Google Spreadsheet to Jira, bringing all the details related to your cards, so you can easily calculate throughput, lead time, flow efficiency, whatever the metrics you want.
Honestly the data quality in Jira round this can be a problem.
~ With a budget: Actionable Agile makes a great plugin that cleans it up. But there'd be a cost of course. https://www.55degrees.se/actionableagile
~ Without: Troy Megennis of Focused Objective is a writer and speaker who specializes in Coaching With Data. You could try his downloadable dashboard (requires Excel)
https://www.focusedobjective.com/w/support/
It also does Monte Carlo simulations
I'm a programmer who is interested in processing and analyzing time-series data. I know basic statistics and math, but I'm afraid that's all.
Can you please recommend good books and/or articles that does not require Ph.D. to understand them?
As for my concrete tasks - I want to be able to spot trends, eliminate outliers, be able to make predictions and calculate stats over a range of values. We have quite a bit of events coming off our systems.
I started reading "Introduction to Time Series and Forecasting" by Brockwell and Davis - and I'm completely lost in math.
update on outliers by outliers I mean data points that doesn't necessarily make sense. e.g. the exchange rate is 1.5$(+-10 cents) for a pound on average, but a guy around the corner offers 1.09$ and says he's completely legit.
I've found the NIST Engineering Statistics Handbook's chapter on time series to be a simple and clear introduction to basic time series modeling. It discusses exponential smoothing, auto-regressive, moving average, and eventually ARMA time series modeling. These can be used for trend analysis and possibly prediction, subject to validation.
Outlier/anomaly detection is a much different task; the NIST book doesn't have much on this. It would be helpful to know what kind of outliers you are trying to detect.
I've gone through numerous books and articles and here are my findings. May be they will help others like me.
Regarding theory - I found an article An Introductory Study on Time Series Modeling and Forecasting very well written. That doesn't mean I understood all of its contents, but it's a really good overview of available time series models.
If you're like me and like to see some actual code - there's article series on QuantStart. Examples are in R, but I guess many of them are portable to Python.
I can highly recommend QuantStart blog by Michael Halls-Moore, I found articles easy to read and the author has done a great job trying not to overwhelm a reader with math. I also read Michael's first book and it's a good one for a beginner in the space like me.
Textbooks on the topic are extremely hard for me to read. I tried Time Series Analysis by Hamilton, but haven't gotten far.
Regarding outlier detection I mentioned - I've found this question on SO and its stats counterpart. By the looks of it, it's not something you can study and implement in a couple of evenings, at least not for me.
I'm looking for reliable features for classification of cell types in microscope images. I wonder what is the best approach.
1) I've tried the approach described by Pontil & Verii - using each pixel of normalized images as a feature. It is easy to implement, but the results are not fully satisfactory. And another problem is - the classification is done with some kind of statistic magic and I can't understand why some results are bad.
2) I've tried to extract high level features such as peaks, holes. My implementation is slow, but the advantage is I understand why one cell is identified as such and another not, as you can visualize these features in test images.
3) Recently I've found in an article such features:
angular second-order,
distance, contrast, entropy, anti-difference distance, relevant, mean
of sum, mean of difference, entropy of sum, entropy of difference,
variance, variance of sum, variance of difference.
I wonder whether there are some standard libraries for the extraction of these features (preferably in C/C++) ?
Is there a catalogue of feature-types with pros/cons, use-case description, etc?
Thank you for any suggestion in advance!
I can recommend this article by Lindblad et al, published in the scientific journal Cytometry. It covers some aspects of feature extraction and classification of cells. It does not utilize any standard libraries for feature extraction/classification, but it contains some information on how to build a classifier based on general features.
This might not solve your problem completely, but I hope it might help you move towards a better solution.
You should try Gabor feature extraction technique as it is supposed to extract features very similar to human visual cortical cells...by setting filters at different orientation and scale and then extracting features from each set-up .
you can Start learning from Wikipedea
I think that the Insight Segmentation and Registration Toolkit (ITK) or Visualization Toolkit (VTK) would work well.
Some other options (that might not necessarily include all the features you want) are
http://opencv.org/
http://gdal.org/
http://www.vips.ecs.soton.ac.uk/index.php?title=VIPS
http://www.xdp.it/cximage.htm
Finally I've found what I've searched for and would like to share:
https://sites.google.com/site/cvonlinewiki/home/geometric-feature-extraction-methods
The list looks pretty mature and complete.
EDIT
Another good article for features in biological cells is:
A feature set for cytometry on digitized
microscopic images
A good description of shape features:
http://www.math.uci.edu/icamp/summer/research_11/park/shape_descriptors_survey.pdf
I'm doing time series analysis and I want to build an ARIMAX-model for my data. I was just curious if someone could give me any recommendations on whether to use System Ide. or Econometrics toolbox in Matlab? Which one would you prefer for general time series analysis?
jjepsuomi,
You get what you pay for. What you are trying to do is really complicated. I would suggest not trying to reinvent the wheel, but buying software that can handle the complexities of denominator structure on your causals plus outliers like pulses, level shifts, changes in trend and seasonality. I would recommend looking at SAS, Autobox, SPSS. We developed Autobox.
As of MATLAB 2013b, you cannot compile ARMAX() of the System Identification Toolbox into a standalone application. As this was no-go for us we moved to arima() of the Econometrics Toolbox.