forecasting

问题 I downloaded a TESLA stock from www.nasdaq.com; and after I downloaded the CSV file I realized that I need convert the CSV by using Microsoft Excel 2016. I use the Data Tab; and click Text to Columns. The header is clear now, they are: date, close, volume, open, high, low. Please see the csv file here. LinK: https://drive.google.com/open?id=1cirQi47U4uumvA14g6vOmgsXbV-YvS4l Preview (The CSV data is from 02/02/2017 until 02/02/2018): 1. date | close | volume | open | high | low | 2. 02/02/2018

问题 I am trying to plot a faceted grid of timeseries plots (ideally 3X3) using a list of forecast timeseries data. The data is nested within a list and is of class forecast::forecast. > class(forecasts) [1] "list" > class(forecasts$`1_1`) [1] "forecast" > head(forecasts, 2) $`1_1` Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 Dec 2016 7.370299 7.335176 7.405422 7.316583 7.424015 $`1_10` Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 Dec 2016 7.396656 7.359845 7.433467 7.340359 7.452953 I would like to plot the

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 have a dataset of Total Sales from 2008-2015. I have an entry for each day, and so I have a created a pandas DataFrame with a DatetimeIndex and a column for sales. So it looks like this The problem is that I am missing data for most of 2010. These missing values are currently represented by 0.0 so if I plot the DataFrame I get I want to try forecast values for 2016, possibly using an ARIMA model, so the first step I took was to perform a decomposition of this time series Obviously if I

问题 I have a dataset of Total Sales from 2008-2015. I have an entry for each day, and so I have a created a pandas DataFrame with a DatetimeIndex and a column for sales. So it looks like this The problem is that I am missing data for most of 2010. These missing values are currently represented by 0.0 so if I plot the DataFrame I get I want to try forecast values for 2016, possibly using an ARIMA model, so the first step I took was to perform a decomposition of this time series Obviously if I

问题 I am trying to understand the different between two forecasting package forecast and fable , as the two editions of the same book (second edition and third edition seems to imply that the two packages are equivalent. library(dplyr) raw <- c(44.4082519001086, 47.1074380165289, 43.5633367662204, 43.1003584229391, 42.5828970331588, 38.3217993079585, 38.5751520417029) # raw <- c(raw,rev(raw)) forecast.df <- ts(raw) forecast::autoplot(forecast.df) + forecast::autolayer(forecast::holt(forecast.df

问题 I am trying to understand the different between two forecasting package forecast and fable , as the two editions of the same book (second edition and third edition seems to imply that the two packages are equivalent. library(dplyr) raw <- c(44.4082519001086, 47.1074380165289, 43.5633367662204, 43.1003584229391, 42.5828970331588, 38.3217993079585, 38.5751520417029) # raw <- c(raw,rev(raw)) forecast.df <- ts(raw) forecast::autoplot(forecast.df) + forecast::autolayer(forecast::holt(forecast.df

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 have a csv file consisting of last 3 years of timeseries monthly data. Based on today's date, I would like to read only the previous 2 years of data for forecasting the future. Data file example (has data from 01-01-15 to 31-10-19): Date,Value 01-01-17,2 01-02-17,5 01-03-17,8 01-04-17,4 01-05-17,2 01-06-17,9 01-07-17,8 01-08-17,7 01-09-17,5 01-10-17,1 01-11-17,2 01-12-17,3 01-01-18,5 01-02-18,6 01-03-18,8 01-04-18,2 01-05-18,5 01-06-18,6 Desired result: If today's date is 01/01/19, I want

问题 I have a csv file consisting of last 3 years of timeseries monthly data. Based on today's date, I would like to read only the previous 2 years of data for forecasting the future. Data file example (has data from 01-01-15 to 31-10-19): Date,Value 01-01-17,2 01-02-17,5 01-03-17,8 01-04-17,4 01-05-17,2 01-06-17,9 01-07-17,8 01-08-17,7 01-09-17,5 01-10-17,1 01-11-17,2 01-12-17,3 01-01-18,5 01-02-18,6 01-03-18,8 01-04-18,2 01-05-18,5 01-06-18,6 Desired result: If today's date is 01/01/19, I want