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, 77 insertions, 0 deletions

diff --git a/lib/linear_regression_slope.c b/lib/linear_regression_slope.c
new file mode 100644
index 0000000..d20ca8e
--- /dev/null
+++ b/lib/linear_regression_slope.c
@@ -0,0 +1,77 @@
+// Linear Regression Slope
+//
+// linear_regression_slope
+//
+// Linear regression slope
+//
+// b = Σ(x-x̄)(y-ȳ) / Σ(x-x̄)²
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 0
+
+
+#include "ts.h"
+
+typedef struct {
+    uint_t k;
+} ts_linear_regression_slope_params_t;
+
+ts_parameter_t const ts_linear_regression_slope_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_linear_regression_slope_params_t) };
+
+uint_t ts_linear_regression_slope_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( c ? ((ts_linear_regression_slope_params_t *)c)->k - 1 : 0 );
+}
+
+int ts_linear_regression_slope(
+	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_linear_regression_slope_p );
+	if ( !c ) return -1;
+    uint_t k = ((ts_linear_regression_slope_params_t *)c)->k;
+    if (k == 0 || n < k) return -1;
+
+    fp_t sum_x = 0, sum_x2 = 0;
+    for ( uint_t j = 0; j < k; ++j )
+    {
+        fp_t xv = (fp_t)j;
+        sum_x += xv;
+        sum_x2 += xv * xv;
+    }
+    fp_t denom = (fp_t)k * sum_x2 - sum_x * sum_x;
+    fp_t fk = (fp_t)k;
+
+    fp_t sum_y = 0, sum_xy = 0;
+    for ( uint_t j = 0; j < k; ++j )
+    {
+        fp_t yv = x[0][j];
+        sum_y += yv;
+        sum_xy += (fp_t)j * yv;
+    }
+    if (denom == 0) {
+        y[0][k - 1] = 0;
+    } else {
+        y[0][k - 1] = (fk * sum_xy - sum_x * sum_y) / denom;
+    }
+
+    for ( uint_t i = k; i < n; ++i )
+    {
+        fp_t y_new = x[0][i];
+        fp_t y_old = x[0][i - k];
+        fp_t sum_y_old = sum_y;
+        sum_y = sum_y - y_old + y_new;
+        sum_xy = sum_xy - sum_y_old + y_old + (fk - 1) * y_new;
+        if (denom == 0) {
+            y[0][i] = 0;
+        } else {
+            y[0][i] = (fk * sum_xy - sum_x * sum_y) / denom;
+        }
+    }
+	return 0;
+}
+