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

1 files changed, 70 insertions, 0 deletions

diff --git a/lib/deviation_standard.c b/lib/deviation_standard.c
new file mode 100644
index 0000000..a2a0a76
--- /dev/null
+++ b/lib/deviation_standard.c
@@ -0,0 +1,70 @@
+// Moving Standard Deviation
+//
+// standard_deviation - sd
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 0
+
+#include <math.h>
+#include "ts.h"
+
+
+typedef struct {
+    uint_t k;
+} ts_deviation_standard_params_t;
+
+ts_parameter_t const ts_deviation_standard_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_deviation_standard_params_t) };
+
+
+uint_t ts_deviation_standard_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_deviation_standard_params_t *)c)->k > 0 ? ((ts_deviation_standard_params_t *)c)->k - 1 : 0 : 0 );
+}
+
+int ts_deviation_standard(
+	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_standard_p );
+	if ( !c ) return -1;
+	TS_CHECK_INPUT(x, x_n, n);
+	uint_t k = ((ts_deviation_standard_params_t *)c)->k;
+	if (k == 0 || n < k) return -1;
+
+	uint_t xi = 0;
+	uint_t yi = k;
+
+	fp_t mean = 0;     // Mean
+	fp_t mean2 = 0;    // Mean of squares
+
+	for( ; xi < k; ++xi )
+	{
+		mean += x[0][xi];
+		mean2 += _ts_square( x[0][xi] );
+	}
+
+    mean /= k;
+	mean2 /= k;
+
+    fp_t variance = mean2 - _ts_square( mean );
+    y[0][k - 1] = variance >= 0 ? sqrt( variance ) : 0;
+
+    fp_t rk = _ts_reciprocal( k );
+
+	for( ; xi < n; ++xi, ++yi )
+	{
+		mean = mean + ( x[0][xi] - x[0][xi - k] ) * rk;
+		mean2 = mean2 + ( _ts_square( x[0][xi] ) - _ts_square( x[0][xi - k] ) ) * rk;
+
+		variance = mean2 - _ts_square( mean );
+		y[0][yi] = variance >= 0 ? sqrt( variance ) : 0;
+	}
+	return 0;
+}
+
+
+