t_runningadjustments: Compare data to moments computed over a time sliding window.
In shabbychef/fromo: Fast Robust Moments

Description Usage Arguments Details Value Time Windowing Note Author(s) References See Also

Computes moments over a sliding window, then adjusts the data accordingly, centering, or scaling, or z-scoring, and so on.

t_running_centered(v, time = NULL, time_deltas = NULL, window = NULL,
  wts = NULL, na_rm = FALSE, min_df = 0L, used_df = 1, lookahead = 0,
  restart_period = 100L, variable_win = FALSE, wts_as_delta = TRUE,
  check_wts = FALSE, normalize_wts = TRUE)

t_running_scaled(v, time = NULL, time_deltas = NULL, window = NULL,
  wts = NULL, na_rm = FALSE, min_df = 0L, used_df = 1, lookahead = 0,
  restart_period = 100L, variable_win = FALSE, wts_as_delta = TRUE,
  check_wts = FALSE, normalize_wts = TRUE)

t_running_zscored(v, time = NULL, time_deltas = NULL, window = NULL,
  wts = NULL, na_rm = FALSE, min_df = 0L, used_df = 1, lookahead = 0,
  restart_period = 100L, variable_win = FALSE, wts_as_delta = TRUE,
  check_wts = FALSE, normalize_wts = TRUE)

t_running_sharpe(v, time = NULL, time_deltas = NULL, window = NULL,
  wts = NULL, lb_time = NULL, na_rm = FALSE, compute_se = FALSE,
  min_df = 0L, used_df = 1, restart_period = 100L, variable_win = FALSE,
  wts_as_delta = TRUE, check_wts = FALSE, normalize_wts = TRUE)

t_running_tstat(v, time = NULL, time_deltas = NULL, window = NULL,
  wts = NULL, lb_time = NULL, na_rm = FALSE, compute_se = FALSE,
  min_df = 0L, used_df = 1, restart_period = 100L, variable_win = FALSE,
  wts_as_delta = TRUE, check_wts = FALSE, normalize_wts = TRUE)

`v`	a vector of data.
`time`	an optional vector of the timestamps of `v`. If given, must be the same length as `v`. If not given, we try to infer it by summing the `time_deltas`.
`time_deltas`	an optional vector of the deltas of timestamps. If given, must be the same length as `v`. If not given, and `wts` are given and `wts_as_delta` is true, we take the `wts` as the time deltas. The deltas must be positive. We sum them to arrive at the times.
`window`	the window size, in time units. if given as finite integer or double, passed through. If `NULL`, `NA_integer_`, `NA_real_` or `Inf` are given, and `variable_win` is true, then we infer the window from the lookback times: the first window is infinite, but the remaining is the deltas between lookback times. If `variable_win` is false, then these undefined values are equivalent to an infinite window. If negative, an error will be thrown.
`wts`	an optional vector of weights. Weights are ‘replication’ weights, meaning a value of 2 is shorthand for having two observations with the corresponding `v` value. If `NULL`, corresponds to equal unit weights, the default. Note that weights are typically only meaningfully defined up to a multiplicative constant, meaning the units of weights are immaterial, with the exception that methods which check for minimum df will, in the weighted case, check against the sum of weights. For this reason, weights less than 1 could cause `NA` to be returned unexpectedly due to the minimum condition. When weights are `NA`, the same rules for checking `v` are applied. That is, the observation will not contribute to the moment if the weight is `NA` when `na_rm` is true. When there is no checking, an `NA` value will cause the output to be `NA`.
`na_rm`	whether to remove NA, false by default.
`min_df`	the minimum df to return a value, otherwise `NaN` is returned. This can be used to prevent e.g. Z-scores from being computed on only 3 observations. Defaults to zero, meaning no restriction, which can result in infinite Z-scores during the burn-in period.
`used_df`	the number of degrees of freedom consumed, used in the denominator of the centered moments computation. These are subtracted from the number of observations.
`lookahead`	for some of the operations, the value is compared to mean and standard deviation possibly using 'future' or 'past' information by means of a non-zero lookahead. Positive values mean data are taken from the future. This is in time units, and so should be a real.
`restart_period`	the recompute period. because subtraction of elements can cause loss of precision, the computation of moments is restarted periodically based on this parameter. Larger values mean fewer restarts and faster, though less accurate results.
`variable_win`	if true, and the `window` is not a concrete number, the computation window becomes the time between lookback times.
`wts_as_delta`	if true and the `time` and `time_deltas` are not given, but `wts` are given, we take `wts` as the `time_deltas`.
`check_wts`	a boolean for whether the code shall check for negative weights, and throw an error when they are found. Default false for speed.
`normalize_wts`	a boolean for whether the weights should be renormalized to have a mean value of 1. This mean is computed over elements which contribute to the moments, so if `na_rm` is set, that means non-NA elements of `wts` that correspond to non-NA elements of the data vector.
`lb_time`	a vector of the times from which lookback will be performed. The output should be the same size as this vector. If not given, defaults to `time`.
`compute_se`	for `running_sharpe`, return an extra column of the standard error, as computed by Mertens' correction.

Given the length n vector x, for a given index i, define x^(i) as the elements of x defined by the sliding time window (see the section on time windowing). Then define mu_i, sigma_i and n_i as, respectively, the sample mean, standard deviation and number of non-NA elements in x^(i).

We compute output vector m the same size as x. For the 'centered' version of x, we have m_i = x_i - mu_i. For the 'scaled' version of x, we have m_i = x_i / sigma_i. For the 'z-scored' version of x, we have m_i = (x_i - mu_i) / sigma_i. For the 't-scored' version of x, we have m_i = sqrt(n_i) mu_i / sigma_i.

We also allow a 'lookahead' for some of these operations. If positive, the moments are computed using data from larger indices; if negative, from smaller indices.

a vector the same size as the input consisting of the adjusted version of the input. When there are not sufficient (non-nan) elements for the computation, NaN are returned.

This function supports time (or other counter) based running computation. Here the input are the data x_i, and optional weights vectors, w_i, defaulting to 1, and a vector of time indices, t_i of the same length as x. The times must be non-decreasing:

t_1 <= t_2 <= ...

It is assumed that t_0 = -∞. The window, W is now a time-based window. An optional set of lookback times are also given, b_j, which may have different length than the x and w. The output will correspond to the lookback times, and should be the same length. The jth output is computed over indices i such that

b_j - W < t_i ≤ b_j.

For comparison functions (like Z-score, rescaling, centering), which compare values of x_i to local moments, the lookbacks may not be given, but a lookahead L is admitted. In this case, the jth output is computed over indices i such that

t_j - W + L < t_i ≤ t_j + L.

If the times are not given, ‘deltas’ may be given instead. If δ_i are the deltas, then we compute the times as

t_i = ∑_{1 ≤ j ≤ i} δ_j.

The deltas must be the same length as x. If times and deltas are not given, but weights are given and the ‘weights as deltas’ flag is set true, then the weights are used as the deltas.

Some times it makes sense to have the computational window be the space between lookback times. That is, the jth output is to be computed over indices i such that

b_{j-1} - W < t_i ≤ b_j.

This can be achieved by setting the ‘variable window’ flag true and setting the window to null. This will not make much sense if the lookback times are equal to the times, since each moment computation is over a set of a single index, and most moments are underdefined.

The moment computations provided by fromo are numerically robust, but will often not provide the same results as the 'standard' implementations, due to differences in roundoff. We make every attempt to balance speed and robustness. User assumes all risk from using the fromo package.

Note that when weights are given, they are treated as replication weights. This can have subtle effects on computations which require minimum degrees of freedom, since the sum of weights will be compared to that minimum, not the number of data points. Weight values (much) less than 1 can cause computations to return NA somewhat unexpectedly due to this condition, while values greater than one might cause the computation to spuriously return a value with little precision.

Steven E. Pav shabbychef@gmail.com

Terriberry, T. "Computing Higher-Order Moments Online." http://people.xiph.org/~tterribe/notes/homs.html

J. Bennett, et. al., "Numerically Stable, Single-Pass, Parallel Statistics Algorithms," Proceedings of IEEE International Conference on Cluster Computing, 2009. https://www.semanticscholar.org/paper/Numerically-stable-single-pass-parallel-statistics-Bennett-Grout/a83ed72a5ba86622d5eb6395299b46d51c901265

Cook, J. D. "Accurately computing running variance." http://www.johndcook.com/standard_deviation.html

Cook, J. D. "Comparing three methods of computing standard deviation." http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation

running_centered, scale

shabbychef/fromo documentation built on April 11, 2021, 11:03 p.m.