I am trying to build a prediction interval around my forecasted time series.
The prediction interval should be higher around the furthest-out prediction to the nearest one predictions or something like the following figure.
But what I get is a similar PI for the whole duration of the forecast. What would be the best approach to replicate something like the above plot?
I am using sklearn to model my time series.
Related
I'm learning Time Series and want to model the daily temperature of a place. However when applying the SARIMA model with seasonal effect factor, I don't think 365 is a reasonable factor as it's a bit too large, and I only have 5 years of data to train. Is there a way to get around this?
I'm thinking smoothing the data might work. Or there will be other methods to remove the seasonality in the dataset.
I have a series that has a linear trend but no seasonality. I have been trying different time series algorithms. I have tried ARIMA using pmdarima and I get good results with 1st order differencing of the series.
Next, I am using Prophet. With the series as is I get a high MAE. So I differenced the series and used Prophet to make predictions. But now the predicted values (yhat) are the differenced values. How do I convert the predicted values in the yhat column to the original scale so that I can calculate MAE and evaluate the model?
Is it even possible? I have tried all the possible solutions, but since this is unlike min-max scaler, I am not able to find a way out of it. Most of the solutions require the first value of the original series to inverse diff the differenced series.
Any help will be appreciated.
I have ~2.6K hours of sales data with a positive linear trend as well as daily and weekly seasonality. See plotted data. I have tried to model the data using SARIMA and TBATS in python. In both cases, I cannot get the predictions to work as I intend.
For SARIMA, the in sample predictions look great, but when I try to forecast into the future, it looks completely wrong.See here for in sample SARIMA predictionsSee here for how poor the out of sample SARIMA predictions are.
For TBATS, the predicted values match the daily and weekly pattern, but is missing the positive trend, despite me forcing so that use_trend = True. See TBATS model prediction here
I have no idea what I'm doing wrong and have been stuck on this for days! Any advice greatly appreciated.
I am trying to apply machine learning algorithm to a dataset which consits of emission of pollutant gas from an engine called SO2(target variable) which is collected over 6 months of time for at a interval of each of 15 mins each.The dataset also do have other independent variables like pressure,vapour etc with time.
Now the question is
should i go for time series modelling like arima for forcasting the So2?
or should i go for randomforest or svm for forecasting?
Thanks
I suggest that you go for time-series modeling instead of SVM. Your SVM would consider i.i.d (independent and identically distributed) samples, and wouldn't consider the information that encapsulated across time.
I would like to know what SPSS does when it computes the UICI and LICI (upper and lower individual confidence interval). I am asking because when we compute "by hand" the same prediction interval for a given individual using the output tables from a simple linear regression we get a slightly different interval (up to 0,005 difference).
I couldn't find online how to get the code used for this command in order to look closer at what SPSS does when we "check" the boxes for mean and individual prediction intervals.
Thanks for your help,
The SPSS Algorithms manual accessible from the Help menu will give you the formulas. Note that a confidence interval is not the same as a prediction interval.