similar to: is a time series regression model a causal forecasting model?

Are there any packages in R that are suitable for doing time series forecasting using neural networks? I have looked in the nnet package and neural package and they both seem geared towards classification. thanks, Spencer [[alternative HTML version deleted]]

Hi all, Does anyone have the knowledge to help me identify a package capable of forecasting a MULTIPLE regression model? i have a model with one one dependant variable and 4 independant variables. i would like to forecast confidence intervals for a few steps ahead...(DENSITY forecasting). PS i can forecast a univariate vector, with package 'forecast', however, i want one that can do

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Hi friends, If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at : CRAN: https://cran.r-project.org/web/packages/PSF/index.html GitHub: https://github.com/neerajdhanraj/PSF How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm and

Hi friends, If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at : CRAN: https://cran.r-project.org/web/packages/PSF/index.html GitHub: https://github.com/neerajdhanraj/PSF How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm and

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Couldn't find this in the archives. I'm fitting a series of historical weather-related data, but would like to use the latest values to forecast. So let's say that I'm using 1970-2000 to fit a model (using fourier terms and arima/auto.arima), but now would like to use the last X values to predict tomorrow's weather. I'm at a loss. All the functions I've come across

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for

SELECTION FOR CONSULTANTS BY THE WORLD BANK REQUEST FOR EXPRESSIONS OF INTEREST All Submissions must be made via the World Bank Group's "eConsult2" system. Please go to: https://wbgeconsult2.worldbank.org/wbgec/index.html Click on "Business Opportunities", and find the following assignment: Assignment Title: 1067014 - Statistical Analyst with Extensive Experience in

1. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec; datalines; 2008 12 13 12 14 13 12 11 15 10 12 12 12 2009 12 13 12 14 13 12 11 15 10 12 12 12 ; run; I converted the above data into the below format to use it in R as it was giving error: asking

Hi, This is the time series data collected from 2001 to 2008 by every month.so,there are 96 entries.I have done basic statistics.I need to find a model fitted to forecast this data.This is the mixedpaper collection for recycling in the campus. 13251 13754 19061 12631 17414 21350 25384 23646 20312 20740 14007 17175 13910 17191 17113 20250 35003 11975 19665 20490 20436

Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 1

Hello, I am totally new in the field of time series analysis and forecasting and R. I read that R is a powerful tool for time series. Could anyone give me navigation what models of time series are availiable in R etc? -- View this message in context: http://r.789695.n4.nabble.com/Time-Series-Analysis-and-Forecasting-tp4646637.html Sent from the R help mailing list archive at Nabble.com.

> > After running a MANOVA test, I used univariate Fs in order to see > which IV(s) was contributing the difference. > However, I have been critised about it, > and the reason I was given is: > > "MANOVA allows you to explore multiple associations but > does not excuse you from tracing out the causal relationships > for each variable. This does

Dear Users, Consider a multivariate time series model: a_1*y(t)-...-a_k*y(t-k)=b+[c_1*z(t)-...-c_j*z(t-j)] i.e., a simple multivariate time series model with one exogenous variable. I would like to know what package can I use to do the following, using R: 1) Select k and j jointly; 2) Estimate the model; 2) Forecast h=4 steps ahead the estimated model; 4) Bootstrap the forecast, since my sample

Hi, i have a weekly data for servers for 62 weeks. want to predict the cpu% for next 5 weeks.I am trying to forecast for many servers at once but with the code i am getting only one week of future forecast for all the servers. Also the week date for the predicted week is showing as the last week of the original data . Need help in two things How can i change the date for the predicted week, and

Hello, I would like to write a program that compute the principal components of a set of data and then 1. Run the dependent variable against the principal components (lagged value) 2. Do prediction , following Stock and Watson (1999) "Forecasting Inflation". All data are time series. Now I can run the program using single factor (first principal component), but I