1535 search results for "time series"

Plotting Time Series data using ggplot2

September 30, 2010
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Plotting Time Series data using ggplot2

There are various ways to plot data that is represented by a time series in R. The ggplot2 package has scales that can handle dates reasonably easily. Fast Tube by Casper As an example consider a data set on the number of views of the you tube channel ramstatvid. A short snippet of the data is shown

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Time Series Regression of Temperature Anomaly Data: 1 – Don’t Use OLS

July 17, 2010
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Time Series Regression of Temperature Anomaly Data: 1 – Don’t Use OLS

Phil Jones Statement (February , 2010) Phil  Jones of the  Climate Research Unit (CRU) responded to a series of questions from the BBC in early February, 2010 (link). Question B dealt with global warming trends in the 1995 – 2009 … Continue r...

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Plot Multiple Time Series using the flow / inkblot / river / ribbon / volcano / hourglass / area / whatchamacallit plots ~ blue whale catch per country w/ ggplot2

June 27, 2010
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Plot Multiple Time Series using the flow / inkblot / river / ribbon / volcano / hourglass / area / whatchamacallit plots ~ blue whale catch per country w/ ggplot2

Ever since I first looked at this NYT visualization by Amanda Cox, I’ve always wanted to reproduce this in R. This is a plot that stacks multiple time series onto one another, with the width of the river/ribbon/hourglass representing the strength at each time. The NYT article used box office revenue as the width of

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The Kalman Filter For Financial Time Series

May 25, 2010
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The Kalman Filter For Financial Time Series

Every now and then I come across a tool that is so bogged down in pages of esoteric mathematical calculations, it becomes difficult to get even a simple grasp of how or why they might be useful. Even worse, you exhaustively search the internet to find ...

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Wavelet Spectrogram Non-Stationary Financial Time Series analysis using R (TTR/Quantmod/dPlR) with USDEUR

April 28, 2010
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Wavelet Spectrogram Non-Stationary Financial Time Series analysis using R (TTR/Quantmod/dPlR) with USDEUR

I've been doing some research lately regarding types of spectral imaging and decomposition techniques that apply to non-stationary signals. As mentioned earlier, one of the major problems with the simple fourier analysis is that the basis functions ext...

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Smoothing time series with R

March 29, 2010
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Smoothing time series with R

Smoothing is a statistical technique that helps you to spot trends in noisy data, and especially to compare trends between two or more fluctuating time series. It's a useful visualization tool that I'm pleased to see cropping up more and more in statistical graphics on the Web -- it's now a staple in econometric charts and is heavily used...

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FFT (Fast Fourier Transform) of time series — promises and pitfalls towards trading

February 24, 2010
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FFT (Fast Fourier Transform) of time series  — promises and pitfalls towards trading

Fig 1. FFT transformed time series (EBAY) reconstructed with first three and twenty harmonics, respectively.I see quite a few traders interested in advanced signal processing techniques. It is often instructive to see why they may or may not be useful....

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Time Series Calendar Heat Maps Using R

February 22, 2010
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Time Series Calendar Heat Maps Using R

I came across an interesting blog that showcased Charting time series as calendar heat maps in R . It is based upon a great algorithm created by Paul Bleicher,CMO of Humedica. I'll let you link to the other blog to see more details on the background ...

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Practical Implementation of Neural Network based time series (stock) prediction -PART 5

February 7, 2010
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Practical Implementation of Neural Network based time series (stock) prediction  -PART 5

Following is an example of what it looks like to predict an actual univariate price series. The period of the signal that was sampled was already in stationary form, so not much massaging was needed other than normalization (described earlier).What's ...

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Practical Implementation of Neural Network based time series (stock) prediction -PART 4

February 4, 2010
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Practical Implementation of Neural Network based time series (stock) prediction  -PART 4

Consider this an introduction to how we need to pre-process the data.I mentioned earlier that a financial time series is typically a unit root or non-stationary signal, what this means is that if you sample statistical properties over time, they will o...

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