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=-385.31 AIC=774.62 AICc=775.19 BIC=776.98 & I get flat forecasts.I have tried using drift but that only helps when forecasting for 2016 & flattens 2017 onward. … Is there something that can be done to overcome this.I have also tried the similar exercise in SAS using proc UCM & that seems to generate forecasts better than the auto.arima. Can someone help out? …

I assumed i will get a forecast where there is an upward trend as there is trend historically along with some variations but what i got is flat forecast. … <- forecast(model,12) plot(forecasts) If i go with the seasonal plot of both historical data along with 2019 forecast these is how it looks The series seems to be forecastable and therefore shouldn't …

I have a AUTO ARIMA fitted on weekly. It gives a forecast as flat line as seen below I tried adding seasonality and stuff, but still the forecast is a flat line. …

ARIMA/SARIMA models are able to follow the variation in training data. But it generates nearly flat prediction for the test period. … fit.test <- model_arima %>% forecast(h=length(ts_test)) I am unable to understand what can be the reason for flat predictions from ARIMA/SARIMA models and how can I fix it? …

models: Holt Winters smoothing, TBATS Smoothing, ARIMA, and AR Neural Nets with the following functions in R, using the "forecast" package: HoltWinters, tbats, auto.arima, nnetar I forecasted 36 periods … The following results: My question is why does the HoltWinters seem to be the only meaningful forecast. I have enough data that getting flat lines for all the other forecasts seems odd. …

I'm trying to forecast a large dataset using ARIMA (The data does not have seasonality), I ended up getting an ARIMA(2,1,2) model where the log of volume was taken due to increase in variance over … model ; proc arima data=intel_stock; identify var= logvolume(1); estimate p = 2 q = 2 ; forecast lead=60 interval=month id=Date out=forecast; run; * Merge forecast and volume into the same dataset and …

I am using an arima model to forecast sales of a given product in python, using statsmodels.tsa.arima.model.ARIMA Sales are daily, with a history of 2019 until today. … The model is adjusting correctly to the past, however when performing the forecast, it returns a flat line, as in the image shown. …

Of course, it is unintuitive that a flat, non-varying, forecasts "explains variation" better than a variable NN forecast. … It is extremely common for flat historical mean forecasts to outperform more complex ones, whether ARIMA or ML-based: Is it unusual for the MEAN to outperform ARIMA? …

Thus, a flat forecast is exactly what an ARIMA(0,1,0) model does. … Fitting an exponential smoothing model, incidentally, also yields a flat forecast: plot(forecast(ets(Russia_ShareofGDP_TS),h=16)) Now, I often argue that a flat forecast my well be the best forecast there …

(Preface) My problem is that when I do the time series forecast with auto.arima(), it gives me a ARIMA(1,1,1) model which generate a flat forecast line as the below figure. … (forecast next quater) model = auto.arima(data_ts) autoplot(forecast(model,13)) However, I assign D = 1 in auto.arima() function according to suggestion from ARIMA forecast straight line? …

I have a very basic question on including constant in Arima models. I'll illustrate this by an example. … Arima with drift or SES with drift. When I do not include a constant, I get a flat forecast which is reasonable for the series at hand. …

ARIMA(1,0,0) with non-zero mean : 98.46206 ARIMA(0,0,1) with non-zero mean : 98.42101 ARIMA(0,0,0) with zero mean : 104.5138 ARIMA(1,0,1) with non-zero mean : 100.3878 there is ARIMA(0,0,1) the … best second model, but when using at forecasting, White Noise will give a flat forecasting (because its definition is not suitable for forecasting as i known so far), so is it a good way to implement …

How do you expect to get anything but a flat forecast? … more data would be better, especially if you want to train this ARIMA model. …

The ARIMA(1,1,2) is the best model with the lowest AICc. I'm in trouble with the point forecast where the straight line is flat. … library(forecast) AA<-Alberta$Male AA1<-ts(AA,start=1921,end=2009,frequency=1) A2<-diff(AA1,1) fit1<-arima(A2, order=c(1,1,0)) fit2 <-arima(A2, order=c(1,1,1)) fit3<-arima(A2, order=c(1,1,2)) fit4<-arima …

Your question is extremely similar to many questions about "flat forecasts" from ARIMA models. Please take a look through those earlier threads. … In your case, the ARIMA forecast converges quickly to the linear trend. Note also that ARIMA - and any other method as well - attempts to separate the signal from the noise. …