series_shapes_fl()

La funzione è una funzione series_shapes_fl()definita dall'utente che rileva una tendenza positiva/negativa o un salto in una serie. Questa funzione accetta una tabella contenente più serie temporali (matrice numerica dinamica) e calcola i punteggi di tendenza e salto per ogni serie. L'output è un dizionario (dinamico) contenente i punteggi.

Sintassi

T | extend series_shapes_fl(, y_seriesAvanzate)

Altre informazioni sulle convenzioni di sintassi.

Parametri

Nome	Tipo	Obbligatoria	Descrizione
y_series	dynamic	✔️	Cella di matrice di valori numerici.
Avanzate	bool		Il valore predefinito è false. Impostare su per true restituire parametri calcolati aggiuntivi.

Definizione di funzione

È possibile definire la funzione incorporando il codice come funzione definita da query o creandola come funzione archiviata nel database, come indicato di seguito:

Query-defined

Definire la funzione usando l'istruzione let seguente. Non sono necessarie autorizzazioni.

Importante

Un'istruzione let non può essere eseguita autonomamente. Deve essere seguita da un'istruzione di espressione tabulare. Per eseguire un esempio funzionante di series_shapes_fl(), vedere Esempio.

let series_shapes_fl=(series:dynamic, advanced:bool=false)
{
    let n = array_length(series);
//  calculate normal dynamic range between 10th and 90th percentiles
    let xs = array_sort_asc(series);
    let low_idx = tolong(n*0.1);
    let high_idx = tolong(n*0.9);
    let low_pct = todouble(xs[low_idx]);
    let high_pct = todouble(xs[high_idx]);
    let norm_range = high_pct-low_pct;
//  trend score
    let lf = series_fit_line_dynamic(series);
    let slope = todouble(lf.slope);
    let rsquare = todouble(lf.rsquare);
    let rel_slope = abs(n*slope/norm_range);
    let sign_slope = iff(slope >= 0.0, 1.0, -1.0);
    let norm_slope = sign_slope*rel_slope/(rel_slope+0.1);  //  map rel_slope from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let trend_score = norm_slope*rsquare;
//  jump score
    let lf2=series_fit_2lines_dynamic(series);
    let lslope = todouble(lf2.left.slope);
    let rslope = todouble(lf2.right.slope);
    let rsquare2 = todouble(lf2.rsquare);
    let split_idx = tolong(lf2.split_idx);
    let last_left = todouble(lf2.left.interception)+lslope*split_idx;
    let first_right = todouble(lf2.right.interception)+rslope;
    let jump = first_right-last_left;
    let rel_jump = abs(jump/norm_range);
    let sign_jump = iff(first_right >= last_left, 1.0, -1.0);
    let norm_jump = sign_jump*rel_jump/(rel_jump+0.1);  //  map rel_jump from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let jump_score1 = norm_jump*rsquare2;
//  filter for jumps that are not close to the series edges and the right slope has the same direction
    let norm_rslope = abs(rslope/norm_range);
    let jump_score = iff((sign_jump*rslope >= 0.0 or norm_rslope < 0.02) and split_idx between((0.1*n)..(0.9*n)), jump_score1, 0.0);
    let res = iff(advanced, bag_pack("n", n, "low_pct", low_pct, "high_pct", high_pct, "norm_range", norm_range, "slope", slope, "rsquare", rsquare, "rel_slope", rel_slope, "norm_slope", norm_slope,
                              "trend_score", trend_score, "split_idx", split_idx, "jump", jump, "rsquare2", rsquare2, "last_left", last_left, "first_right", first_right, "rel_jump", rel_jump,
                              "lslope", lslope, "rslope", rslope, "norm_rslope", norm_rslope, "norm_jump", norm_jump, "jump_score", jump_score)
                              , bag_pack("trend_score", trend_score, "jump_score", jump_score));
    res
};
// Write your query to use the function here.

Archiviata

Definire la funzione archiviata una volta usando il comando seguente .create function. Sono necessarie le autorizzazioni utente del database .

Importante

È necessario eseguire questo codice per creare la funzione prima di poter usare la funzione come illustrato nell'esempio.

.create-or-alter function with (folder = "Packages\\Series", docstring = "Series detector for positive/negative trend or step. Returns a dynamic with trend and jump scores")
series_shapes_fl(series:dynamic, advanced:bool=false)
{
    let n = array_length(series);
//  calculate normal dynamic range between 10th and 90th percentiles
    let xs = array_sort_asc(series);
    let low_idx = tolong(n*0.1);
    let high_idx = tolong(n*0.9);
    let low_pct = todouble(xs[low_idx]);
    let high_pct = todouble(xs[high_idx]);
    let norm_range = high_pct-low_pct;
//  trend score
    let lf = series_fit_line_dynamic(series);
    let slope = todouble(lf.slope);
    let rsquare = todouble(lf.rsquare);
    let rel_slope = abs(n*slope/norm_range);
    let sign_slope = iff(slope >= 0.0, 1.0, -1.0);
    let norm_slope = sign_slope*rel_slope/(rel_slope+0.1);  //  map rel_slope from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let trend_score = norm_slope*rsquare;
//  jump score
    let lf2=series_fit_2lines_dynamic(series);
    let lslope = todouble(lf2.left.slope);
    let rslope = todouble(lf2.right.slope);
    let rsquare2 = todouble(lf2.rsquare);
    let split_idx = tolong(lf2.split_idx);
    let last_left = todouble(lf2.left.interception)+lslope*split_idx;
    let first_right = todouble(lf2.right.interception)+rslope;
    let jump = first_right-last_left;
    let rel_jump = abs(jump/norm_range);
    let sign_jump = iff(first_right >= last_left, 1.0, -1.0);
    let norm_jump = sign_jump*rel_jump/(rel_jump+0.1);  //  map rel_jump from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let jump_score1 = norm_jump*rsquare2;
//  filter for jumps that are not close to the series edges and the right slope has the same direction
    let norm_rslope = abs(rslope/norm_range);
    let jump_score = iff((sign_jump*rslope >= 0.0 or norm_rslope < 0.02) and split_idx between((0.1*n)..(0.9*n)), jump_score1, 0.0);
    let res = iff(advanced, bag_pack("n", n, "low_pct", low_pct, "high_pct", high_pct, "norm_range", norm_range, "slope", slope, "rsquare", rsquare, "rel_slope", rel_slope, "norm_slope", norm_slope,
                              "trend_score", trend_score, "split_idx", split_idx, "jump", jump, "rsquare2", rsquare2, "last_left", last_left, "first_right", first_right, "rel_jump", rel_jump,
                              "lslope", lslope, "rslope", rslope, "norm_rslope", norm_rslope, "norm_jump", norm_jump, "jump_score", jump_score)
                              , bag_pack("trend_score", trend_score, "jump_score", jump_score));
    res
}

Esempio

Query-defined

Per usare una funzione definita da query, richiamarla dopo la definizione della funzione incorporata.

let series_shapes_fl=(series:dynamic, advanced:bool=false)
{
    let n = array_length(series);
//  calculate normal dynamic range between 10th and 90th percentiles
    let xs = array_sort_asc(series);
    let low_idx = tolong(n*0.1);
    let high_idx = tolong(n*0.9);
    let low_pct = todouble(xs[low_idx]);
    let high_pct = todouble(xs[high_idx]);
    let norm_range = high_pct-low_pct;
//  trend score
    let lf = series_fit_line_dynamic(series);
    let slope = todouble(lf.slope);
    let rsquare = todouble(lf.rsquare);
    let rel_slope = abs(n*slope/norm_range);
    let sign_slope = iff(slope >= 0.0, 1.0, -1.0);
    let norm_slope = sign_slope*rel_slope/(rel_slope+0.1);  //  map rel_slope from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let trend_score = norm_slope*rsquare;
//  jump score
    let lf2=series_fit_2lines_dynamic(series);
    let lslope = todouble(lf2.left.slope);
    let rslope = todouble(lf2.right.slope);
    let rsquare2 = todouble(lf2.rsquare);
    let split_idx = tolong(lf2.split_idx);
    let last_left = todouble(lf2.left.interception)+lslope*split_idx;
    let first_right = todouble(lf2.right.interception)+rslope;
    let jump = first_right-last_left;
    let rel_jump = abs(jump/norm_range);
    let sign_jump = iff(first_right >= last_left, 1.0, -1.0);
    let norm_jump = sign_jump*rel_jump/(rel_jump+0.1);  //  map rel_jump from [-Inf, +Inf] to [-1, 1]; 0.1 is a clibration constant
    let jump_score1 = norm_jump*rsquare2;
//  filter for jumps that are not close to the series edges and the right slope has the same direction
    let norm_rslope = abs(rslope/norm_range);
    let jump_score = iff((sign_jump*rslope >= 0.0 or norm_rslope < 0.02) and split_idx between((0.1*n)..(0.9*n)), jump_score1, 0.0);
    let res = iff(advanced, bag_pack("n", n, "low_pct", low_pct, "high_pct", high_pct, "norm_range", norm_range, "slope", slope, "rsquare", rsquare, "rel_slope", rel_slope, "norm_slope", norm_slope,
                              "trend_score", trend_score, "split_idx", split_idx, "jump", jump, "rsquare2", rsquare2, "last_left", last_left, "first_right", first_right, "rel_jump", rel_jump,
                              "lslope", lslope, "rslope", rslope, "norm_rslope", norm_rslope, "norm_jump", norm_jump, "jump_score", jump_score)
                              , bag_pack("trend_score", trend_score, "jump_score", jump_score));
    res
};
let ts_len = 100;
let noise_pct = 2;
let noise_gain = 3;
union
(print tsid=1 | extend y = array_concat(repeat(20, ts_len/2), repeat(150, ts_len/2))),
(print tsid=2 | extend y = array_concat(repeat(0, ts_len*3/4), repeat(-50, ts_len/4))),
(print tsid=3 | extend y = range(40, 139, 1)),
(print tsid=4 | extend y = range(-20, -109, -1))
| extend x = range(1, array_length(y), 1)
//
| extend shapes = series_shapes_fl(y)
| order by tsid asc 
| fork (take 4) (project tsid, shapes)
| render timechart with(series=tsid, xcolumn=x, ycolumns=y)

Archiviata

Importante

Per eseguire correttamente questo esempio, è necessario prima eseguire il codice di definizione della funzione per archiviare la funzione.

let ts_len = 100;
let noise_pct = 2;
let noise_gain = 3;
union
(print tsid=1 | extend y = array_concat(repeat(20, ts_len/2), repeat(150, ts_len/2))),
(print tsid=2 | extend y = array_concat(repeat(0, ts_len*3/4), repeat(-50, ts_len/4))),
(print tsid=3 | extend y = range(40, 139, 1)),
(print tsid=4 | extend y = range(-20, -109, -1))
| extend x = range(1, array_length(y), 1)
//
| extend shapes = series_shapes_fl(y)
| order by tsid asc 
| fork (take 4) (project tsid, shapes)
| render timechart with(series=tsid, xcolumn=x, ycolumns=y)

Output

I rispettivi punteggi di tendenza e salto:

tsid	shapes
1	    {
          "trend_score": 0.703199714530169,
          "jump_score": 0.90909090909090906
        }
2	    {
          "trend_score": -0.51663751343174869,
          "jump_score": -0.90909090909090906
        }
3	    {
          "trend_score": 0.92592592592592582,
          "jump_score": 0.0
        }
4	    {
          "trend_score": -0.92592592592592582,
          "jump_score": 0.0
        }