forecasting

I have the following R code library(forecast) value <- c(1.2, 1.7, 1.6, 1.2, 1.6, 1.3, 1.5, 1.9, 5.4, 4.2, 5.5, 6, 5.6, 6.2, 6.8, 7.1, 7.1, 5.8, 0, 5.2, 4.6, 3.6, 3, 3.8, 3.1, 3.4, 2, 3.1, 3.2, 1.6, 0.6, 3.3, 4.9, 6.5, 5.3, 3.5, 5.3, 7.2, 7.4, 7.3, 7.2, 4, 6.1, 4.3, 4, 2.4, 0.4, 2.4) sensor<-ts(value,frequency=24) fit <- auto.arima(sensor) LH.pred<-predict(fit,n.ahead=24) plot(sensor,ylim=c(0,10),xlim=c(0,5),type="o", lwd="1") lines(LH.pred$pred,col="red",type="o",lwd="1") grid() The resulting graph is But I am not satisfied with the prediction. Is there any way to make the prediction look

I implemented a forecasting model using LSTM in Keras. The dataset is 15mints seperated and I am forecasting for 12 future steps. The model performs good for the problem. But there is a small problem with the forecast made. It is showing a small shift effect. To get a more clear picture see the below attached figure. How to handle this problem.? How the data must be transformed to handle this kind of issue.? The model I used is given below init_lstm = RandomUniform(minval=-.05, maxval=.05) init_dense_1 = RandomUniform(minval=-.03, maxval=.06) model = Sequential() model.add(LSTM(15, input_shape

I'm trying to use tf.contrib.seq2seq module to do forecasting on some data (just float32 vectors) but all the examples I found using the seq2seq module from TensorFlow are used for translation and therefore embeddings. I'm struggling to understand exactly what tf.contrib.seq2seq.Helper is doing for the Seq2Seq architecture and how I can use the CustomHelper in my case. This is what I've done for now: import tensorflow as tf from tensorflow.python.layers import core as layers_core input_seq_len = 15 # Sequence length as input input_dim = 1 # Nb of features in input output_seq_len = forecast_len

I am trying to explain to myself the forecasting result from applying an ARIMA model to a time-series dataset. The data is from the M1-Competition, the series is MNB65. I am trying to fit the data to an ARIMA(1,0,0) model and get the forecasts. I am using R. Here are some output snippets: > arima(x, order = c(1,0,0)) Series: x ARIMA(1,0,0) with non-zero mean Call: arima(x = x, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.9421 12260.298 s.e. 0.0474 202.717 > predict(arima(x, order = c(1,0,0)), n.ahead=12) $pred Time Series: Start = 53 End = 64 Frequency = 1 [1] 11757.39 11786.50 11813.92

A maths problem really I think... I have some historical data for some spreadsheet outputs along with the number of rows and columns. What I'd like to do is use this data to predict the peak memory usage and time taken based on the - known - row and columns. So, if no historical data exists then there will be no predictions. 1 or 2 historical values will be very inaccurate but I hope that given a wide enough variety of historical values, then a reasonably-accurate prediction could be made? I've got a table on a jsfiddle. Any help or ideas would be really appreciated. I don't really know where

Is there a way to specify minimum or maximum possible values in a forecast done with ETS/ARIMA models? Such as when forecasting a trend in % that can only go between 0% and 100%. I am using R package forecast (and function forecast ). 李哲源 If your time series y has a natural bound [a, b] , you should take a "logit-alike" transform first: f <- function (x, a, b) log((x - a) / (b - x)) yy <- f(y, a, b) Then the resulting yy is unbounded on (-Inf, Inf) , suitable for Gaussian error assumption. Use yy for time series modelling, and take back-transform later on the prediction / forecast: finv <-

I have made a simple time-series, i added a little noise to a sin function and tried to decompose it using the "stl" and "decompose" function in R, while my series definitely has more than 2 period and is periodic, R gives me the following error for both functions: x [1] 1.4537365796 2.7185844368 2.8394728999 3.8926989923 4.3405508086 5.1959080871 [7] 5.6602505790 5.4829985648 5.6357660330 4.6084976233 4.6617322922 4.0286486832 [13] 3.3641752333 1.7408063182 0.8815147612 0.2895139342 -0.5402768515 -1.5612641107 [19] -2.1584502547 -2.9878043526 -3.5545638149 -4.0530074199 -4.0748538612 -4