Question

How would you use Armadillo to perform calculations involving time series data and what functions are available for smoothing and filtering?

Answer 1

Armadillo is a C++ linear algebra library that can be used for time series data calculations. To use Armadillo, first include the library in your code and link it during compilation.

For time series analysis, create an arma::vec or arma::mat object to store the data. Use functions like .load() or .set_size() to initialize the object with data or size. For example:

#include <armadillo>

using namespace arma;

int main() {

vec my_series;

my_series.load("data.csv", csv_ascii);

}

Armadillo provides several functions for smoothing and filtering time series data:

1. Moving average: Use conv_to::from(conv(my_series, ones(N, 1)/N)) where N is the window size.
2. Exponential smoothing: Implement custom function using iterative formula: S_t = alpha * X_t + (1 – alpha) * S_(t-1).
3. Median filter: Apply medfilt1 from mlpack library on Armadillo vector.
4. Gaussian filter: Convolve time series with Gaussian kernel using conv().
5. Kalman filter: Utilize kf_predict and kf_update functions from third-party libraries like Bayes++ or BFL.
6. Savitzky-Golay filter: Employ sg_filter from third-party libraries like savgol.

Choose appropriate smoothing/filtering technique based on data characteristics and desired output.