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Slope of time series (xts) object over rolling window


Slope of time series (xts) object over rolling window

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Slope of time series (xts) object over rolling window
Tag : r , By : Tom D
Date : January 12 2021, 09:11 PM

it should still fix some issue It works for me. We don't need summary nor by.column (there is only one column anyways).
The resulting range is c(-90, 90) to 3 decimals.
library(xts)

f <- function (x) {
  res <- lm(x ~ time(x))
  atan(coef(res)[[2]]) * 180 / pi
}

r <- rollapplyr(pk, 14, f)
round(range(r, na.rm = TRUE), 3)
## [1] -90  90

r <- rollapplyr(k * 14 * pk / max(pk), 14, f)

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Slope of time series (xts) object over rolling window , Slope , time , series , (xts) , object , over , rolling , window ,

Rolling window from time-series

Tag : pandas , By : LUK
Date : March 29 2020, 07:55 AM
will help you I am wondering if there is a simple and neat way to create a rolling window representation from a time-series data, using Pandas (etc)? , How about: 
pd.DataFrame({i: x.shift(-i) for i in range(5)}).dropna()

Time series with scala and spark. Rolling window

Tag : scala , By : Thomas Plunkett
Date : March 29 2020, 07:55 AM
help you fix your problem This is a perfect application for window-functions. By using rangeBetween you can set your sliding window to 20s. Note that in the example below no partitioning is specified (no partitionBy). Without a partitioning, this code will not scale:
import ss.implicits._

val df = Seq(
  (225, 1.5),
  (245, 0.5),
  (300, 2.4),
  (319, 1.2),
  (320, 4.6)
).toDF("seconds", "value")

val window = Window.orderBy($"seconds").rangeBetween(-20L, 0L) // add partitioning here

df
  .withColumn("num_row_in_window", sum(lit(1)).over(window))
  .withColumn("sum_values_in_window", sum($"value").over(window))
  .show()

+-------+-----+-----------------+--------------------+
|seconds|value|num_row_in_window|sum_values_in_window|
+-------+-----+-----------------+--------------------+
|    225|  1.5|                1|                 1.5|
|    245|  0.5|                2|                 2.0|
|    300|  2.4|                1|                 2.4|
|    319|  1.2|                2|                 3.6| 
|    320|  4.6|                3|                 8.2|
+-------+-----+-----------------+--------------------+

Rolling window over irregular time series

Tag : r , By : Vick Aita
Date : March 29 2020, 07:55 AM

optimized rolling functions on irregular time series with time-based window

Tag : r , By : TomL
Date : March 29 2020, 07:55 AM

R: Compute a rolling sum on irregular time series grouped by id variables with time-based window

Tag : r , By : ThF
Date : March 29 2020, 07:55 AM
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