diff options
Diffstat (limited to 'src/statistical.c')
| -rw-r--r-- | src/statistical.c | 459 |
1 files changed, 0 insertions, 459 deletions
diff --git a/src/statistical.c b/src/statistical.c deleted file mode 100644 index d4b33fd5a..000000000 --- a/src/statistical.c +++ /dev/null @@ -1,459 +0,0 @@ -#include "common.h" - -// -------------------------------------------------------------------------------------------------------------------- - -inline LONG_DOUBLE sum_and_count(const LONG_DOUBLE *series, size_t entries, size_t *count) { - if(unlikely(entries == 0)) { - if(likely(count)) - *count = 0; - - return NAN; - } - - if(unlikely(entries == 1)) { - if(likely(count)) - *count = (isnan(series[0])?0:1); - - return series[0]; - } - - size_t i, c = 0; - LONG_DOUBLE sum = 0; - - for(i = 0; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - c++; - sum += value; - } - - if(likely(count)) - *count = c; - - if(unlikely(c == 0)) - return NAN; - - return sum; -} - -inline LONG_DOUBLE sum(const LONG_DOUBLE *series, size_t entries) { - return sum_and_count(series, entries, NULL); -} - -inline LONG_DOUBLE average(const LONG_DOUBLE *series, size_t entries) { - size_t count = 0; - LONG_DOUBLE sum = sum_and_count(series, entries, &count); - - if(unlikely(count == 0)) - return NAN; - - return sum / (LONG_DOUBLE)count; -} - -// -------------------------------------------------------------------------------------------------------------------- - -LONG_DOUBLE moving_average(const LONG_DOUBLE *series, size_t entries, size_t period) { - if(unlikely(period <= 0)) - return 0.0; - - size_t i, count; - LONG_DOUBLE sum = 0, avg = 0; - LONG_DOUBLE p[period]; - - for(count = 0; count < period ; count++) - p[count] = 0.0; - - for(i = 0, count = 0; i < entries; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - - if(unlikely(count < period)) { - sum += value; - avg = (count == period - 1) ? sum / (LONG_DOUBLE)period : 0; - } - else { - sum = sum - p[count % period] + value; - avg = sum / (LONG_DOUBLE)period; - } - - p[count % period] = value; - count++; - } - - return avg; -} - -// -------------------------------------------------------------------------------------------------------------------- - -static int qsort_compare(const void *a, const void *b) { - LONG_DOUBLE *p1 = (LONG_DOUBLE *)a, *p2 = (LONG_DOUBLE *)b; - LONG_DOUBLE n1 = *p1, n2 = *p2; - - if(unlikely(isnan(n1) || isnan(n2))) { - if(isnan(n1) && !isnan(n2)) return -1; - if(!isnan(n1) && isnan(n2)) return 1; - return 0; - } - if(unlikely(isinf(n1) || isinf(n2))) { - if(!isinf(n1) && isinf(n2)) return -1; - if(isinf(n1) && !isinf(n2)) return 1; - return 0; - } - - if(unlikely(n1 < n2)) return -1; - if(unlikely(n1 > n2)) return 1; - return 0; -} - -inline void sort_series(LONG_DOUBLE *series, size_t entries) { - qsort(series, entries, sizeof(LONG_DOUBLE), qsort_compare); -} - -inline LONG_DOUBLE *copy_series(const LONG_DOUBLE *series, size_t entries) { - LONG_DOUBLE *copy = mallocz(sizeof(LONG_DOUBLE) * entries); - memcpy(copy, series, sizeof(LONG_DOUBLE) * entries); - return copy; -} - -LONG_DOUBLE median_on_sorted_series(const LONG_DOUBLE *series, size_t entries) { - if(unlikely(entries == 0)) - return NAN; - - if(unlikely(entries == 1)) - return series[0]; - - if(unlikely(entries == 2)) - return (series[0] + series[1]) / 2; - - LONG_DOUBLE avg; - if(entries % 2 == 0) { - size_t m = entries / 2; - avg = (series[m] + series[m + 1]) / 2; - } - else { - avg = series[entries / 2]; - } - - return avg; -} - -LONG_DOUBLE median(const LONG_DOUBLE *series, size_t entries) { - if(unlikely(entries == 0)) - return NAN; - - if(unlikely(entries == 1)) - return series[0]; - - if(unlikely(entries == 2)) - return (series[0] + series[1]) / 2; - - LONG_DOUBLE *copy = copy_series(series, entries); - sort_series(copy, entries); - - LONG_DOUBLE avg = median_on_sorted_series(copy, entries); - - freez(copy); - return avg; -} - -// -------------------------------------------------------------------------------------------------------------------- - -LONG_DOUBLE moving_median(const LONG_DOUBLE *series, size_t entries, size_t period) { - if(entries <= period) - return median(series, entries); - - LONG_DOUBLE *data = copy_series(series, entries); - - size_t i; - for(i = period; i < entries; i++) { - data[i - period] = median(&series[i - period], period); - } - - LONG_DOUBLE avg = median(data, entries - period); - freez(data); - return avg; -} - -// -------------------------------------------------------------------------------------------------------------------- - -// http://stackoverflow.com/a/15150143/4525767 -LONG_DOUBLE running_median_estimate(const LONG_DOUBLE *series, size_t entries) { - LONG_DOUBLE median = 0.0f; - LONG_DOUBLE average = 0.0f; - size_t i; - - for(i = 0; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - - average += ( value - average ) * 0.1f; // rough running average. - median += copysignl( average * 0.01, value - median ); - } - - return median; -} - -// -------------------------------------------------------------------------------------------------------------------- - -LONG_DOUBLE standard_deviation(const LONG_DOUBLE *series, size_t entries) { - if(unlikely(entries < 1)) - return NAN; - - if(unlikely(entries == 1)) - return series[0]; - - size_t i, count = 0; - LONG_DOUBLE sum = 0; - - for(i = 0; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - - count++; - sum += value; - } - - if(unlikely(count == 0)) - return NAN; - - if(unlikely(count == 1)) - return sum; - - LONG_DOUBLE average = sum / (LONG_DOUBLE)count; - - for(i = 0, count = 0, sum = 0; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - - count++; - sum += powl(value - average, 2); - } - - if(unlikely(count == 0)) - return NAN; - - if(unlikely(count == 1)) - return average; - - LONG_DOUBLE variance = sum / (LONG_DOUBLE)(count - 1); // remove -1 to have a population stddev - - LONG_DOUBLE stddev = sqrtl(variance); - return stddev; -} - -// -------------------------------------------------------------------------------------------------------------------- - -LONG_DOUBLE single_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha) { - size_t i, count = 0; - LONG_DOUBLE level = 0, sum = 0; - - if(unlikely(isnan(alpha))) - alpha = 0.3; - - for(i = 0; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - count++; - - sum += value; - - LONG_DOUBLE last_level = level; - level = alpha * value + (1.0 - alpha) * last_level; - } - - return level; -} - -// -------------------------------------------------------------------------------------------------------------------- - -// http://grisha.org/blog/2016/02/16/triple-exponential-smoothing-forecasting-part-ii/ -LONG_DOUBLE double_exponential_smoothing(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE *forecast) { - size_t i, count = 0; - LONG_DOUBLE level = series[0], trend, sum; - - if(unlikely(isnan(alpha))) - alpha = 0.3; - - if(unlikely(isnan(beta))) - beta = 0.05; - - if(likely(entries > 1)) - trend = series[1] - series[0]; - else - trend = 0; - - sum = series[0]; - - for(i = 1; i < entries ; i++) { - LONG_DOUBLE value = series[i]; - if(unlikely(isnan(value) || isinf(value))) continue; - count++; - - sum += value; - - LONG_DOUBLE last_level = level; - - level = alpha * value + (1.0 - alpha) * (level + trend); - trend = beta * (level - last_level) + (1.0 - beta) * trend; - } - - if(forecast) - *forecast = level + trend; - - return level; -} - -// -------------------------------------------------------------------------------------------------------------------- - -/* - * Based on th R implementation - * - * a: level component - * b: trend component - * s: seasonal component - * - * Additive: - * - * Yhat[t+h] = a[t] + h * b[t] + s[t + 1 + (h - 1) mod p], - * a[t] = α (Y[t] - s[t-p]) + (1-α) (a[t-1] + b[t-1]) - * b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1] - * s[t] = γ (Y[t] - a[t]) + (1-γ) s[t-p] - * - * Multiplicative: - * - * Yhat[t+h] = (a[t] + h * b[t]) * s[t + 1 + (h - 1) mod p], - * a[t] = α (Y[t] / s[t-p]) + (1-α) (a[t-1] + b[t-1]) - * b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1] - * s[t] = γ (Y[t] / a[t]) + (1-γ) s[t-p] - */ -static int __HoltWinters( - const LONG_DOUBLE *series, - int entries, // start_time + h - - LONG_DOUBLE alpha, // alpha parameter of Holt-Winters Filter. - LONG_DOUBLE beta, // beta parameter of Holt-Winters Filter. If set to 0, the function will do exponential smoothing. - LONG_DOUBLE gamma, // gamma parameter used for the seasonal component. If set to 0, an non-seasonal model is fitted. - - const int *seasonal, - const int *period, - const LONG_DOUBLE *a, // Start value for level (a[0]). - const LONG_DOUBLE *b, // Start value for trend (b[0]). - LONG_DOUBLE *s, // Vector of start values for the seasonal component (s_1[0] ... s_p[0]) - - /* return values */ - LONG_DOUBLE *SSE, // The final sum of squared errors achieved in optimizing - LONG_DOUBLE *level, // Estimated values for the level component (size entries - t + 2) - LONG_DOUBLE *trend, // Estimated values for the trend component (size entries - t + 2) - LONG_DOUBLE *season // Estimated values for the seasonal component (size entries - t + 2) -) -{ - if(unlikely(entries < 4)) - return 0; - - int start_time = 2; - - LONG_DOUBLE res = 0, xhat = 0, stmp = 0; - int i, i0, s0; - - /* copy start values to the beginning of the vectors */ - level[0] = *a; - if(beta > 0) trend[0] = *b; - if(gamma > 0) memcpy(season, s, *period * sizeof(LONG_DOUBLE)); - - for(i = start_time - 1; i < entries; i++) { - /* indices for period i */ - i0 = i - start_time + 2; - s0 = i0 + *period - 1; - - /* forecast *for* period i */ - xhat = level[i0 - 1] + (beta > 0 ? trend[i0 - 1] : 0); - stmp = gamma > 0 ? season[s0 - *period] : (*seasonal != 1); - if (*seasonal == 1) - xhat += stmp; - else - xhat *= stmp; - - /* Sum of Squared Errors */ - res = series[i] - xhat; - *SSE += res * res; - - /* estimate of level *in* period i */ - if (*seasonal == 1) - level[i0] = alpha * (series[i] - stmp) - + (1 - alpha) * (level[i0 - 1] + trend[i0 - 1]); - else - level[i0] = alpha * (series[i] / stmp) - + (1 - alpha) * (level[i0 - 1] + trend[i0 - 1]); - - /* estimate of trend *in* period i */ - if (beta > 0) - trend[i0] = beta * (level[i0] - level[i0 - 1]) - + (1 - beta) * trend[i0 - 1]; - - /* estimate of seasonal component *in* period i */ - if (gamma > 0) { - if (*seasonal == 1) - season[s0] = gamma * (series[i] - level[i0]) - + (1 - gamma) * stmp; - else - season[s0] = gamma * (series[i] / level[i0]) - + (1 - gamma) * stmp; - } - } - - return 1; -} - -LONG_DOUBLE holtwinters(const LONG_DOUBLE *series, size_t entries, LONG_DOUBLE alpha, LONG_DOUBLE beta, LONG_DOUBLE gamma, LONG_DOUBLE *forecast) { - if(unlikely(isnan(alpha))) - alpha = 0.3; - - if(unlikely(isnan(beta))) - beta = 0.05; - - if(unlikely(isnan(gamma))) - gamma = 0; - - int seasonal = 0; - int period = 0; - LONG_DOUBLE a0 = series[0]; - LONG_DOUBLE b0 = 0; - LONG_DOUBLE s[] = {}; - - LONG_DOUBLE errors = 0.0; - size_t nb_computations = entries; - LONG_DOUBLE *estimated_level = callocz(nb_computations, sizeof(LONG_DOUBLE)); - LONG_DOUBLE *estimated_trend = callocz(nb_computations, sizeof(LONG_DOUBLE)); - LONG_DOUBLE *estimated_season = callocz(nb_computations, sizeof(LONG_DOUBLE)); - - int ret = __HoltWinters( - series, - (int)entries, - alpha, - beta, - gamma, - &seasonal, - &period, - &a0, - &b0, - s, - &errors, - estimated_level, - estimated_trend, - estimated_season - ); - - LONG_DOUBLE value = estimated_level[nb_computations - 1]; - - if(forecast) - *forecast = 0.0; - - freez(estimated_level); - freez(estimated_trend); - freez(estimated_season); - - if(!ret) - return 0.0; - - return value; -} |