forecasting

I am forecasting on a large set of time series (5,000+). I would like to do this with a hierarchical approach were I do the forecasting at a higher level and then allocate the forecast down to each SKU. I see a need for doing this in order to zoom into a lower geographic level of detail, while doing the forecasting on a higher level (top-down). For example, below you see a sample of the structure that I am thinking about. Total => Europe => Netherlands => RegionA => Client_A_in_Netherlands => SKU1 => SKU2 => SKU3 => Client_Q_in_Netherlands => SKU15 => Germany1 => (...) => ClientY_in_Germany =>

I have one sample data Sno period year_quarter country city sales_revenue 1 1/1/2009 2009-Q1 Argentina Buenos Aires 3008 2 1/4/2009 2009-Q2 Argentina Buenos Aires 3244 3 1/7/2009 2009-Q3 Argentina Buenos Aires 8000 4 1/10/2009 2009-Q4 Argentina Buenos Aires 8719 5 1/1/2010 2010-Q1 Argentina Buenos Aires 3008 6 1/4/2010 2010-Q2 Argentina Buenos Aires 3244 7 1/7/2010 2010-Q3 Argentina Buenos Aires 78 8 1/10/2010 2010-Q4 Argentina Buenos Aires 7379 9 1/1/2011 2011-Q1 Argentina Buenos Aires 3735 10 1/4/2011 2011-Q2 Argentina Buenos Aires 7339 11 1/7/2011 2011-Q3 Argentina Buenos Aires 17240 12 1

With the forecast package, I have a time series that I would like ?auto.arima to automatically pick the orders but I would like to coerce seasonality. The defaults for the function allow for the seasonal argument to be set to TRUE , but that only allows the option for seasonality not a coercion. auto.arima(x, d=NA, D=NA, max.p=5, max.q=5, max.P=2, max.Q=2, max.order=5, max.d=2, max.D=1, start.p=2, start.q=2, start.P=1, start.Q=1, stationary=FALSE, seasonal=TRUE, ic=c("aicc", "aic", "bic"), stepwise=TRUE, trace=FALSE, approximation=(length(x)>100 | frequency(x)>12), xreg=NULL, test=c("kpss",

I want to use Gaussian Processes to solve a regression task. My data is as follow : each X vector has a length of 37, and each Y vector has a length of 8. I'm using the sklearn package in Python but trying to use gaussian processes leads to an Exception : from sklearn import gaussian_process print "x :", x__ print "y :", y__ gp = gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1) gp.fit(x__, y__) x : [[ 136. 137. 137. 132. 130. 130. 132. 133. 134. 135. 135. 134. 134. 1139. 1019. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 70. 24. 55. 0. 9. 0. 0.] [ 136. 137. 137. 132. 130

I have a .csv file containing a 5-year time series, with hourly resolution (commoditiy price). Based on the historical data, I want to create a forecast of the prices for the 6th year. I have read a couple of articles on the www about these type of procedures, and I basically based my code on the code posted there, since my knowledge in both Python (especially statsmodels) and statistic is at most limited. Those are the links, for those who are interested: http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/ http://www.johnwittenauer.net/a-simple-time-series-analysis

I've written a function to iteratively forecast models built using the package dyn, and I'd like some feedback on it. Is there a better way to do this? Has someone written canonical "forecast" methods for the dyn class (or dynlm class), or am I venturing into uncharted territory here? ipredict <-function(model, newdata, interval = "none", level = 0.95, na.action = na.pass, weights = 1) { P<-predict(model,newdata=newdata,interval=interval, level=level,na.action=na.action,weights=weights) for (i in seq(1,dim(newdata)[1])) { if (is.na(newdata[i])) { if (interval=="none") { P[i]<-predict(model

I tried forecasting with holt-winters model as shown below but I keep getting a prediction that is not consistent with what I expect. I also showed a visualization of the plot Train = Airline[:130] Test = Airline[129:] from statsmodels.tsa.holtwinters import Holt y_hat_avg = Test.copy() fit1 = Holt(np.asarray(Train['Passengers'])).fit() y_hat_avg['Holt_Winter'] = fit1.predict(start=1,end=15) plt.figure(figsize=(16,8)) plt.plot(Train.index, Train['Passengers'], label='Train') plt.plot(Test.index,Test['Passengers'], label='Test') plt.plot(y_hat_avg.index,y_hat_avg['Holt_Winter'], label='Holt