time-series

问题 I have been reading about Keras RNN models (LSTMs and GRUs), and authors seem to largely focus on language data or univariate time series that use training instances composed of previous time steps. The data I have is a bit different. I have 20 variables measured every year for 10 years for 100,000 persons as input data, and the 20 variables measured for year 11 as output data. What I would like to do is predict the value of one of the variables (not the other 19) for the 11th year. I have my

问题 For a simple time series: import pandas as pd df = pd.DataFrame({'dt':['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-05', '2020-01-06'], 'foo':[1,2, 4,5,6]}) df['dt'] = pd.to_datetime(df.dt) df['dt_label']= df['dt'].dt.strftime('%Y-%m-%d %a') df = df.set_index('dt') #display(df) df['foo'].plot() x =plt.xticks(ticks=df.reset_index().dt.values, labels=df.dt_label, rotation=90, horizontalalignment='right') How can I highlight the x-axis labels for weekends? edit Pandas Plots: Separate color

问题 I wanna combine three graphics on one graph. The data from inside of R which is " nottem ". Can someone help me to write code to put a seasonal mean and harmonic (cosine model) and its time series plots together by using different colors? I already wrote model code just don't know how to combine them together to compare. Code :library(TSA) nottem month.=season(nottem) model=lm(nottem~month.-1) summary(nottem) har.=harmonic(nottem,1) model1=lm(nottem~har.) summary(model1) plot(nottem,type="l"

问题 I am using the R programming language. Using the following tutorial : https://plotly.com/r/time-series/ and this stackoverflow question : How to plot multiple series/lines in a time series using plotly in R? I was able to make an interactive time series plot: library(xts) library(ggplot2) library(dplyr) library(plotly) library(lubridate) #time series 1 date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") property_damages_in_dollars <- rnorm(731,100,10) final_data <-

问题 I try to interpolate this meterValue, full csv here: https://drive.google.com/open?id=18cwtw-chAB-FqqCesXZJ-6NB6eHFJlgQ localminute,dataid,meter_value 2015-10-03 09:51:53,6578,157806 2015-10-13 13:41:49,6578,158086 : : 2016-01-17 16:00:33,6578,164544 #end of meter_value data for ID=6578 Based on what @G. Grothendieck, suggested, and I got error at z.interpolate (merging data) D6578z <- read.csv.zoo("test_6578.csv")[,2] D6578zd <- to.daily(D6578z)[,4] #Warning messages: #1: In zoo(xx, order.by

问题 I would like to do the reverse operation explained in this question: convert data frame with date column to time series. For example the decomposition of the co2 data is a multi variate time series: m <- decompose(co2) > str(m) List of 6 $ x : Time-Series [1:468] from 1959 to 1998: 315 316 316 318 318 ... $ seasonal: Time-Series [1:468] from 1959 to 1998: -0.0536 0.6106 1.3756 2.5168 3.0003 ... $ trend : Time-Series [1:468] from 1959 to 1998: NA NA NA NA NA ... $ random : Time-Series [1:468]

问题 I have the following R matrix: Date MyVal 2016 1 2017 2 2018 3 .... 2026 10 What I want to do is "blow it up" so that it goes like this (where monthly values are linearly interpolated): Date MyVal 01/01/2016 1 02/01/2016 .. .... 01/01/2017 2 .... 01/01/2026 10 I realize I can easily generate the sequence using: DateVec <- seq(as.Date(paste(minYear,"/01/01", sep = "")), as.Date(paste(maxYear, "/01/01", sep = "")), by = "month") And I can use that to make a large matrix and then fill things in

问题 I am trying to replicate this example of time series analysis in R using Keras (see Here) and unfortunately I am receiving error message while computing first average rmes coln <- colnames(compare_train)[4:ncol(compare_train)] cols <- map(coln, quo(sym(.))) rsme_train <- map_dbl(cols, function(col) rmse( compare_train, truth = value, estimate = !!col, na.rm = TRUE )) %>% mean() rsme_train Error message: Error in is_symbol(x) : object '.' not found There are some helpful comments at the bottom

问题 I am trying to replicate this example of time series analysis in R using Keras (see Here) and unfortunately I am receiving error message while computing first average rmes coln <- colnames(compare_train)[4:ncol(compare_train)] cols <- map(coln, quo(sym(.))) rsme_train <- map_dbl(cols, function(col) rmse( compare_train, truth = value, estimate = !!col, na.rm = TRUE )) %>% mean() rsme_train Error message: Error in is_symbol(x) : object '.' not found There are some helpful comments at the bottom

问题 I have the data: dput(head(data)) > dput(head(data)) structure(list(Gmt.time = c("01.06.2015 00:00", "01.06.2015 00:01", "01.06.2015 00:02", "01.06.2015 00:03", "01.06.2015 00:04", "01.06.2015 00:05" ), Open = c(0.88312, 0.88337, 0.88377, 0.88412, 0.88393, 0.8838 ), High = c(0.88337, 0.88378, 0.88418, 0.88418, 0.88393, 0.88393 ), Low = c(0.883, 0.88337, 0.88374, 0.88394, 0.88368, 0.88362 ), Close = c(0.88337, 0.88375, 0.88412, 0.88394, 0.8838, 0.88393 ), Volume = c(83.27, 100.14, 117.18, 52