time-series

问题 How do I fit this model in R, step by step? My scope is to make a forecast for t+1. Y(t) = αX(t) + βY(t-1) Y(t) <- years from 1900 to 2000. X <- a score measure from 0 to 100. Y(t-1) <- lagged value of order 1 for Y. Thanks in advance. 回答1: Your model is an AR(1) time series for y with covariate x . We can just use arima0 (no missing value) or arima (missing value allowed) from R base: fit <- arima0(y, order = c(1, 0, 0), xreg = x) Let's consider a small example: set.seed(0) x <- runif(100) #

问题 I've seen this done with the Panda Timeseries, but was hoping to get some help with Dataframes. I have a file of monthly values from 1966-2009. I do not have data for the year 1985 and would like to add data for 2010/2011 as well. These additions would simply have NaNs attached to them. With the code below, I'm trying to cut my dataset so that it starts at 1980 and then add in the years that are missing with NaN values attached. However, nothing gets cut and nothing is added. Is there

问题 I've a graph created in Networkx and plotted using plotly Code: import numpy as np import pandas as pd import networkx as nx import matplotlib.pyplot as plt import plotly.graph_objects as go from pprint import pprint from collections import OrderedDict def get_edge_trace(G): edge_x = [] edge_y = [] for edge in G.edges(): x0, y0 = G.nodes[edge[0]]['pos'] x1, y1 = G.nodes[edge[1]]['pos'] edge_x.append(x0) edge_x.append(x1) edge_x.append(None) edge_y.append(y0) edge_y.append(y1) edge_y.append

问题 I have a times series with some missing entries, that looks like this: date value --------------- 2000 5 2001 10 2003 8 2004 72 2005 12 2007 13 I would like to do create a column for the "previous_value". But I only want it to show values for consecutive years. So I want it to look like this: date value previous_value ------------------------------- 2000 5 nan 2001 10 5 2003 8 nan 2004 72 8 2005 12 72 2007 13 nan However just applying pandas shift function directly to the column 'value' would

问题 I have the CSV data as follows: A_ID P_ID 1429982904 1430370002 1430974801 1431579602 1432184403 1432789202 1435208402 1435308653 11Jgipc qjMakF 364 365 363 363 364 364 364 367 11Jgipc qxL8FJ 18 18 18 18 18 18 18 18 11Jgipc r0Bpnt 40 40 41 41 41 42 42 42 11Jgipc roLk4N 140 140 143 143 146 147 147 149 11Jgipc tOudhM 12 13 13 13 13 13 14 14 11Jgipc u-x6o8 678 678 688 688 689 690 692 695 11Jgipc u5HHmV 1778 1785 1811 1811 1819 1826 1834 1836 11Jgipc ufrVoP 67 67 67 67 67 67 67 67 11Jgipc vRqMK4

问题 I have the CSV data as follows: A_ID P_ID 1429982904 1430370002 1430974801 1431579602 1432184403 1432789202 1435208402 1435308653 11Jgipc qjMakF 364 365 363 363 364 364 364 367 11Jgipc qxL8FJ 18 18 18 18 18 18 18 18 11Jgipc r0Bpnt 40 40 41 41 41 42 42 42 11Jgipc roLk4N 140 140 143 143 146 147 147 149 11Jgipc tOudhM 12 13 13 13 13 13 14 14 11Jgipc u-x6o8 678 678 688 688 689 690 692 695 11Jgipc u5HHmV 1778 1785 1811 1811 1819 1826 1834 1836 11Jgipc ufrVoP 67 67 67 67 67 67 67 67 11Jgipc vRqMK4

问题 I have the CSV data as follows: A_ID P_ID 1429982904 1430370002 1430974801 1431579602 1432184403 1432789202 1435208402 1435308653 11Jgipc qjMakF 364 365 363 363 364 364 364 367 11Jgipc qxL8FJ 18 18 18 18 18 18 18 18 11Jgipc r0Bpnt 40 40 41 41 41 42 42 42 11Jgipc roLk4N 140 140 143 143 146 147 147 149 11Jgipc tOudhM 12 13 13 13 13 13 14 14 11Jgipc u-x6o8 678 678 688 688 689 690 692 695 11Jgipc u5HHmV 1778 1785 1811 1811 1819 1826 1834 1836 11Jgipc ufrVoP 67 67 67 67 67 67 67 67 11Jgipc vRqMK4

问题 Using R I would like to compare the RMSE (root mean square error) from two prediction models. The first model uses estimates from 1966 to 2000 to predict 2001 and then uses estimates from 1966 to 2001 to predict 2002 and so on up to 2015. The second model uses estimates from 1991 to 2000 to predict 2001 and then uses estimates from 1992 to 2001 to predict 2002 and so on up to 2015. This problem has me really stumped and I truly appreciate any help. DF <- data.frame(YEAR=1966:2015, TEMP=rnorm

问题 Using R I would like to compare the RMSE (root mean square error) from two prediction models. The first model uses estimates from 1966 to 2000 to predict 2001 and then uses estimates from 1966 to 2001 to predict 2002 and so on up to 2015. The second model uses estimates from 1991 to 2000 to predict 2001 and then uses estimates from 1992 to 2001 to predict 2002 and so on up to 2015. This problem has me really stumped and I truly appreciate any help. DF <- data.frame(YEAR=1966:2015, TEMP=rnorm

问题 Using R I would like to compare the RMSE (root mean square error) from two prediction models. The first model uses estimates from 1966 to 2000 to predict 2001 and then uses estimates from 1966 to 2001 to predict 2002 and so on up to 2015. The second model uses estimates from 1991 to 2000 to predict 2001 and then uses estimates from 1992 to 2001 to predict 2002 and so on up to 2015. This problem has me really stumped and I truly appreciate any help. DF <- data.frame(YEAR=1966:2015, TEMP=rnorm