# Best option for time series with Python matplotlib

Best option for time series with Python matplotlib

There are 3 coding samples below which demo auto scaling for dates in Python’s MatPlotLib. Best one appears to be the last one.

Sourced links are listed as well

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```#http://stackoverflow.com/questions/29461608/matplotlib-fixing-x-axis-scale-and-autoscale-y-axis

# import numpy as np
# import matplotlib.pyplot as plt
#
# x, y = np.arange(0,101,1) ,300 - 0.1*np.arange(0,101,1)
# mask = (x >= 50) & (x <= 100)
#
# fig, ax = plt.subplots()
#
# plt.show()

#http://stackoverflow.com/questions/32972371/how-to-show-date-and-time-on-x-axis-in-matplotlib
# import matplotlib.pyplot as plt
# import pandas as pd
# import matplotlib.dates as mdates
#
# times = pd.date_range('2015-10-06', periods=500, freq='10min')
#
# fig, ax = plt.subplots(1)
# fig.autofmt_xdate()
# plt.plot(times, range(times.size))
#
# xfmt = mdates.DateFormatter('%d-%m-%y %H:%M')
# ax.xaxis.set_major_formatter(xfmt)
#
# plt.show()

#this one works best and easiest it seems
#http://blog.mafr.de/2012/03/11/time-series-data-with-matplotlib/
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

converters={ 0: mdates.strpdate2num('%Y-%m-%d')})

plt.plot_date(x=days, y=impressions, fmt="r-")
plt.title("Pageessions on example.com")
plt.ylabel("Page impressions")
plt.grid(True)
plt.show()```
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# Use time series analysis using R forecast package

Use time series analysis using R forecast package

I have tried this with no amazing success but some may find it useful

http://www.datavizualization.com/blog/time-series-analysis-using-r-forecast-package

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# Does IBM Watson Analytics handle Time Series? Now what?

This Postgres with GPU is a neat idea below

How Does IBM Watson Analytics handle Time Series? Now what?

This was emailed to my FIRST CONTACT here at QuantLabs.net 5 years ago.  Thanks to him for sending! We had  the prez of this software put on a presentation:

In 2011, IBM Watson shook our world when it beat Ken Jennings on Jeopardy and “Computer beats Man” was the reality we needed to accept.

,,,

https://quantlabs.net/blog/2015/01/how-does-ibm-watson-analytics-handle-time-series-now-what/

Another high speed open source in memory database option? Postgres with GPU backend

This was pointed a few weeks ago but this is just another but I want to verify that Redis is still the more viable option due to it being less maintenace with somewhat complicated coding with GPU or CUDA

https://quantlabs.net/blog/2015/01/another-high-speed-open-source-in-memory-database-option-postgres-with-gpu-backend/

Bryan

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# How Does IBM Watson Analytics handle Time Series? Now what?

How Does IBM Watson Analytics handle Time Series? Now what?

This was emailed to my FIRST CONTACT here at QuantLabs.net 5 years ago.  Thanks to him for sending! We had  the prez of this software put on a presentation:

In 2011, IBM Watson shook our world when it beat Ken Jennings on Jeopardy and “Computer beats Man” was the reality we needed to accept.

IBM’s WatsonAnalytics is now avalilabe for a 30 day trial and it did not shake my world when it came to time series analysis.  They have a free trial to download and play with the tool. You just need to create a spreadsheet with a header record with a name and the data below in a column and then upload the data very easily into the web based tool.

It took two example time series for me to wring my hands and say in my head, “Man beats Computer”.  Sherlock Holmes said, “It’s Elementary my dear Watson”.  I can say, “It is not Elementary Watson and requires more than pure number crunching using NN or whatever they have”.

The first example is our classic time series 1,9,1,9,1,9,1,5 to see if Watson could identify the change in the pattern and mark it as an outlier(ie inlier) and continue to forecast 1,9,1,9, etc.  It did not.  In fact, it expected a causal variable to be present so I take it that Watson is not able to handle Univariate problems, but if anyone else knows differently please let me know.

The second example was originally presented in the 1970 Box-Jenkin’s text book and is a causal problem referred to as “Gas Furnace” and is described in detail in the textbook and also on NIST.GOV‘s website.  Methane is the X variable and Y is the Carbon Dioxide output.  If you know or now closely examine the model on the NIST website, you will see a complicated relationship where there is a complicated relationship between X and Y that occurs with a delay between the impact of X and the effect on Y (see Yt-1 and Yt-2 and Xt-1 and Xt-2 in the equation).  Note that the R Squared is above 99.4%!  Autobox is able to model this complex relationship uniquely and automatically.  Try it out for yourself here! The GASX problem can be found in the “BOXJ” folder which comes with every installed version of Autobox for Windows.

Watson did not find this relationship and offered a predictive strength of only 27%(see the X on the left hand of the graph) compared to 96.4%.  Not very good. This is why we benchmark. Please try this yourself and let me know if you see something different here.

Autobox’s model has lags in Y and lags in the X from 0 to 7 periods and finds an outlier(which can occur even in simulated data out of randomness).  We show you the model output here in a “regression” model format so it can be understood more easily. We will present the Box-Jenkins version down below.

Here is a more parsimonious version of the Autobox model in pure Box-Jenkins notation.  Another twist is that Autobox found that the variance increased at period 185 and used Weighted Least Squares to do the analysis hence you will see the words “General Linear Model” at the top of the report.

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# Advanced Time Series Analysis with a Depth Look at ARIMA function and its constituents with Rogue Wave Software

Advanced Time Series Analysis with a Depth Look at ARIMA  function and its constituents with Rogue Wave Software
From the NYC Contact so thanks to him

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# IS TeaFiles and DiscreteLogics a decent cheaper alternative to SQL Server 2014 with market data internal memory for time series analysis?

IS TeaFiles and DiscreteLogics a decent alternative to SQL Server 2014 with market data internal memory for time series analysis? It is obviously more affordable for most people as well.

This was spawned by a comment at

This works with all the popular programming languages like C#, C++, Python, R, and Matlab. Exciting as it is quite affordable as well.  It is obviously more affordable for most people as well.

http://discretelogics.com/

Let me know what you think.

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# Could OxyPlot be the best open source plotting package for Microsoft Windows dotnet and c# for time series analysis of big data?

This has been recommended by the world famous London quant:

http://oxyplot.codeplex.com

Thanks to him!

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# Is this the best Microsoft C# DOTNET and C based API for numeric bases time series analysis modelling with charting?

Is this the best Microsoft C# DOTNET and C based API for numeric bases time series analysis modelling with charting?

This was sent from my London based quant friend:

http://www.roguewave.com/products/imsl-numerical-libraries/.net-library.aspx

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Other options from my Youtube subscriber so thanks to him:

The main 3 math libraries are NAG (nag is the most popular), IMSL and GSL.

Here is the nag toolbox for .net:
http://www.nag.com/numeric/DT/DTdescription.asp

NAG also has a toolbox for matlab.

http://www.nag.com/numeric/MB/start.asp

GSL is open-source and doesn’t have native c# support, although someone developed a c# wrapper for it:

Supposedly, you can also use pinvoke to call the DLLs.

GNU MP big number library, which is used for large number computations is not a joke. Mathematica, Maple and Matlab all incorporate the code (it is LGPL licensed) into their programs.

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# Webinar this Monday Mar 18 on advanced time series analysis with charting and client graphical tools

Hi there
As I have stated, I have now completed the analysis with videos and sample source code on Matlab’s Econometrics and Finance toolboxes. These are the main core models for modern and accurate trading strategies used by the pros at banks and hedge funds.   I am now moving onto the Math related toolboxes which the Professional and Institutional market participants use. This video listed below summarizes this:
As stated earlier, I have an EXCLUSIVE online event for my QuantLabs.net Premium Membership happening this Monday Mar 18 at 7PM EST as well.
The most recent topics with video and source code included:
Visual financial time series
Time series demo with Matlab Finance Toolbox
Financial time series GUI tool client demo
As you can imagine, I am quickly adding further content on these math related toolboxes over the next week. As a result, I will be doing another LIVE EXCLUSIVE online event on these as well.
Many other Membership benefits listed here.
Thanks
Bryan

series, analysis ,charting, client, graphical  tools

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# Youtube video on Intro to C++ Boost Accumulator for financial time series and analysis like a moving average

Youtube video on Intro to C++ Boost Accumulator for financial time series and analysis like a moving average