forecasting

问题 I have a time series problem that is a little modified. I have 2 indexed variables, date and user id. for each user id, date, i want to forecast a value. The interesting part is the date resets for each new user id. Standard time series problem have for this time period, forecast the next n days. In my train data, i have for each user id, for jan 1-3, i have their target value. In my test data, for each user id, test date is jan 4-6. For both the train and test data, index of the dataframe is

问题 I am building a forecasting model sing R utilizing stl() function. I have monthly data for 2 years, that's 24 observations, 2 period. Now stl() won't allow me to decompose my data saying that I have less than the required minimum observation. I check the code and yes, it needs to be 24 + 1 or 2 period + 1. My question is why it needs extra observation? decompose() function needs only at least 2 period. 来源： https://stackoverflow.com/questions/48723342/stl-function-minimum-observation

问题 Pity Google BigQuery still doesn't have a function such as forecast() that we see in Spreadsheets-- don't look down on yet; given one has the statistical know-how, surprising amount of smoothing and seasonality can be added to forecasting on spreadsheets. BigQuery allows you to determine Standard Deviation, correlation and intercept metrics. Using that, one can create the prediction model-- refer to this and this. But that uses Linear regression model; so we are not happy with the seasonality

问题 I load a dataframe (named stock) with this data: day value 2000-12-01 00:00:00 11.809242 2000-12-01 06:00:00 10.919792 2000-12-01 12:00:00 13.265208 2000-12-01 18:00:00 13.005139 2000-12-02 00:00:00 10.592222 2000-12-02 06:00:00 8.873160 2000-12-02 12:00:00 12.292847 2000-12-02 18:00:00 12.609722 2000-12-03 00:00:00 11.378299 2000-12-03 06:00:00 10.510972 2000-12-03 12:00:00 8.297222 2000-12-03 18:00:00 8.110486 2000-12-04 00:00:00 8.066154 I try to implement forecasting using ets() model

问题 I've been doing a variety of models in R with time series data (in XTS format) and I keep running into the same issue where there's no date / time component to the fitted values / forecasts and thus I can't graph them on the same graph as the original data. Using the following code, from everything I see online I SHOULD get two line graphs on the same graph. However, I just get the original graph from the plot command and the second line never appears. library("stats") library("forecast")

问题 I'm struggling with forecasting out of sample values with an ARMAX model. Fitting the model works fine. armax_mod31 = sm.tsa.ARMA(endog = sales, order = (3,1), exog = media).fit() armax_mod31.fittedvalues Forecasting without exogenous values, as far as I have an according model, works fine as well. arma_mod31 = sm.tsa.ARMA(sales, (3,1)).fit() all_arma = arma_mod31.forecast(steps = 14, alpha = 0.05) forecast_arma = Series(res_arma[0], index = pd.date_range(start = "2013-08-21", periods = 14))

问题 I have a gold price data set with "DATE" and "GOLD PRICE" variables.After doing all the pre processing steps in R,I convert the data frame object to time series by ts or xts function and check for stationary through adf test. Now by enabling forecast library I run auto.arima function and forecast next ten values. x <- "DATE" "GOLD PRICE" 01-01-2006 1326 x.xts <- xts(x$GOLD PRICE,X$DATE), fit <- auto.arima(x.xts) forecast <- forecast(fit,h=10) Now when I plot the forecast I get some values

问题 I create an forecasting experiment using R engine. My data source is pivoted, hence I need to pass row by row. The output works great with single row prediction. But when I try to populate multiple lines, it still gives single row output - for the first record only. I'm trying to loop my result as follows : # Map 1-based optional input ports to variables dataset1 <- maml.mapInputPort(1) # class: data.frame library(forecast) library(reshape) library(dplyr) library(zoo) #exclude non required

问题 I want to predict the future values for my simple moving average model. I used the following procedure: x <- c(14,10,11,7,10,9,11,19,7,10,21,9,8,16,21,14,6,7) df <- data.frame(x) dftimeseries <- ts(df) library(TTR) smadf <- SMA(dftimeseries, 4) # lag is 4 library(forecast) forecasteddf <- forecast(smadf, 4) # future 4 values When run the above code, my forecast values are the same for all the next 4 days. Am I coding it correctly? Or, am I conceptually wrong? The same is the case with

问题 When trying to compose a function from smaller ones using Rob Hyndman's forecast library, like so: > library('forecast') > arf <- function(data, ...) forecast(ar(data, order.max=1, method="ols"), ...) I get an error when trying to plug in some data: > arf(ts(1:100, start=c(2000,1), frequency=4)) Error in ts(x, frequency = 1, start = 1) : object is not a matrix However, using the body of arf directly works perfectly: > forecast(ar(ts(1:100, start=c(2000,1), frequency=4), order.max=1,method=