run_var: Calculate the trailing mean and variance of streaming _time...
In algoquant/HighFreq: High Frequency Time Series Management
run_var R Documentation
Calculate the trailing mean and variance of streaming time series of
data using an online recursive formula.
Description
Calculate the trailing mean and variance of streaming time series of
data using an online recursive formula.
Usage
run_var(tseries, lambda)
Arguments
tseries
A time series or a matrix of data.
lambda
A decay factor which multiplies past estimates.
Details
The function run_var() calculates the trailing mean and variance
of streaming time series of data r_t, by recursively
weighting the past variance estimates \sigma^2_{t-1}, with the
squared differences of the data minus its trailing means (r_t -
\bar{r}_t)^2, using the decay factor \lambda:
\bar{r}_t = \lambda \bar{r}_{t-1} + (1-\lambda) r_t
\sigma^2_t = \lambda \sigma^2_{t-1} + (1-\lambda) (r_t - \bar{r}_t)^2
Where r_t are the streaming data, \bar{r}_t are the trailing
means, and \sigma^2_t are the trailing variance estimates.
The above online recursive formulas are convenient for processing live
streaming data because they don't require maintaining a buffer of past
data.
The formulas are equivalent to a convolution with exponentially decaying
weights, but they're much faster to calculate.
Using exponentially decaying weights is more natural than using a sliding
look-back interval, because it gradually "forgets" about the past data.
The value of the decay factor \lambda must be in the range between
0 and 1.
If \lambda is close to 1 then the decay is weak and past
values have a greater weight, and the trailing variance values have a
stronger dependence on past data. This is equivalent to a long
look-back interval.
If \lambda is much less than 1 then the decay is strong and
past values have a smaller weight, and the trailing variance values have a
weaker dependence on past data. This is equivalent to a short look-back
interval.
The function run_var() performs the same calculation as the
standard R function
stats::filter(x=series,
filter=weightv, method="recursive"), but it's several times faster.
The function run_var() returns a matrix with two columns and
the same number of rows as the input argument tseries.
The first column contains the trailing means and the second contains the
variance.
Value
A matrix with two columns and the same number of rows as the
input argument tseries. The first column contains the trailing
means and the second contains the variance.
algoquant/HighFreq documentation built on Oct. 26, 2024, 9:20 p.m.
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| run_var | R Documentation |
Calculate the trailing mean and variance of streaming time series of data using an online recursive formula.
Description
Calculate the trailing mean and variance of streaming time series of data using an online recursive formula.
Usage
run_var(tseries, lambda)
Arguments
tseries |
A time series or a matrix of data. |
lambda |
A decay factor which multiplies past estimates. |
Details
The function run_var() calculates the trailing mean and variance
of streaming time series of data r_t, by recursively
weighting the past variance estimates \sigma^2_{t-1}, with the
squared differences of the data minus its trailing means (r_t -
\bar{r}_t)^2, using the decay factor \lambda:
\bar{r}_t = \lambda \bar{r}_{t-1} + (1-\lambda) r_t
\sigma^2_t = \lambda \sigma^2_{t-1} + (1-\lambda) (r_t - \bar{r}_t)^2
Where r_t are the streaming data, \bar{r}_t are the trailing
means, and \sigma^2_t are the trailing variance estimates.
The above online recursive formulas are convenient for processing live streaming data because they don't require maintaining a buffer of past data. The formulas are equivalent to a convolution with exponentially decaying weights, but they're much faster to calculate. Using exponentially decaying weights is more natural than using a sliding look-back interval, because it gradually "forgets" about the past data.
The value of the decay factor \lambda must be in the range between
0 and 1.
If \lambda is close to 1 then the decay is weak and past
values have a greater weight, and the trailing variance values have a
stronger dependence on past data. This is equivalent to a long
look-back interval.
If \lambda is much less than 1 then the decay is strong and
past values have a smaller weight, and the trailing variance values have a
weaker dependence on past data. This is equivalent to a short look-back
interval.
The function run_var() performs the same calculation as the
standard R functionstats::filter(x=series,
filter=weightv, method="recursive"), but it's several times faster.
The function run_var() returns a matrix with two columns and
the same number of rows as the input argument tseries.
The first column contains the trailing means and the second contains the
variance.
Value
A matrix with two columns and the same number of rows as the
input argument tseries. The first column contains the trailing
means and the second contains the variance.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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