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, 56 insertions, 0 deletions

diff --git a/lib/average_exponential.c b/lib/average_exponential.c
new file mode 100644
index 0000000..bae9d5f
--- /dev/null
+++ b/lib/average_exponential.c
@@ -0,0 +1,56 @@
+// Exponential Moving Average
+//
+// average_exponential - ema
+//
+// Weighted moving average with exponentially decreasing weights determined by
+// the constant c
+//
+// y[0] = x[0]
+// y[t] = a * x[t] + ( 1 - a ) * EMA[t - 1]
+// a = 1 / k;
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 1
+
+
+#include <math.h>
+#include "ts.h"
+
+
+typedef struct {
+    fp_t k;
+} ts_average_exponential_params_t;
+
+ts_parameter_t const ts_average_exponential_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_average_exponential_params_t) };
+
+
+uint_t ts_average_exponential_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( 0 );
+}
+
+int ts_average_exponential(
+	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_average_exponential_p );
+	if ( !c ) return -1;
+	TS_CHECK_INPUT(x, x_n, n);
+	fp_t k = ((ts_average_exponential_params_t *)c)->k;
+	if (k <= 0 || isnan(k)) return -1;
+
+	fp_t a = 1.0 / k;
+
+	y[0][0] = x[0][0];
+
+	for( uint_t i = 1; i < n; ++i )
+	{
+		y[0][i] = y[0][i - 1] + a * ( x[0][i] - y[0][i - 1] );
+	}
+	return 0;
+}
+