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| author | Ethan Werner <mail@ethanwerner.com> | 2026-07-01 09:42:39 +0400 |
|---|---|---|
| committer | Ethan Werner <mail@ethanwerner.com> | 2026-07-01 09:42:39 +0400 |
| commit | 07e18043145f3d91c8ea7ec3ad41b3c905585277 (patch) | |
| tree | 2bdeae46973f4bdfce263679a4008ac768011f28 /lib/deviation_average.c | |
initial commit: ts time series analysis library v0.1.0
- 354 financial and mathematical time-series indicators
- Uniform C11 API with consistent function signatures
- 349 passing tests with -Werror
- Static and shared library builds
- pkg-config integration
- Python bindings
- Fuzzing and sanitizer support
- Security hardening flags
Diffstat (limited to 'lib/deviation_average.c')
| -rw-r--r-- | lib/deviation_average.c | 78 |
1 files changed, 78 insertions, 0 deletions
diff --git a/lib/deviation_average.c b/lib/deviation_average.c new file mode 100644 index 0000000..5ac7109 --- /dev/null +++ b/lib/deviation_average.c @@ -0,0 +1,78 @@ +// Moving Mean Deviation +// +// mean_deviation - md +// +// Inputs: 1 +// Outputs: 1 +// Constants: 0 + + +#include <math.h> +#include "ts.h" + + +typedef struct { + uint_t k; +} ts_deviation_average_params_t; + +ts_parameter_t const ts_deviation_average_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_deviation_average_params_t) }; + + +uint_t ts_deviation_average_offset( ts_constant_t const *c ) +{ + ( void )c; + return ( c ? ((ts_deviation_average_params_t *)c)->k > 0 ? ((ts_deviation_average_params_t *)c)->k - 1 : 0 : 0 ); +} + +int ts_deviation_average( + fp_t * const *y, uint_t y_n, + fp_t const * const *x, uint_t x_n, + uint_t n, ts_constant_t const *c +) +{ + TS_PROLOGUE( ts_deviation_average_p ); + if ( !c ) return -1; + uint_t k = ((ts_deviation_average_params_t *)c)->k; + if (k == 0 || n < k) return -1; + + uint_t xi = 0; + uint_t yi = k - 1; + + fp_t mean = 0; + + for( ; xi < k; ++xi ) + { + mean += x[0][xi]; + } + + fp_t rk = _ts_reciprocal( k ); + mean = rk * mean; + + { + fp_t sum = 0; + for( uint_t i = 0; i < k; i++ ) + { + fp_t tmp = x[0][i] - mean; + sum += fabs( tmp ); + } + y[0][yi] = sum * rk; + } + + for( xi = k, yi = k; xi < n; ++xi, ++yi ) + { + mean = mean + ( x[0][xi] - x[0][xi - k] ) * rk; + fp_t sum = 0; + + for( uint_t i = xi - k + 1; i <= xi; i++ ) + { + fp_t tmp = x[0][i] - mean; + sum += fabs( tmp ); + } + + y[0][yi] = sum * rk; + } + return 0; +} + + + |