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26. You should use a split based on time to avoid the look-ahead bias. Train/validation/test in this order by time. The test set should be the most recent part of data. You need to simulate a situation in a production environment, where after training a model you evaluate data coming after the time of creation of the model.
3. I have a monthly sales data set from 2018 January onwards. I would like to know from expert what is the optimum train test split and minimum train test split. Also to mention that my data includes 2020 year data where the sales have been influenced due to pandemic, and 2021 have been recovering year. time-series. forecasting. arima. validation.
This is to test whether two time series are the same. This approach is only suitable for infrequently sampled data where autocorrelation is low. If time series x is the similar to time series y then the variance of x-y should be less than the variance of x. We can test this using a one sided F test for variance.
$\begingroup$ Generally, yes, you could see each subjects data as time series. In practice though, longitudinal data often has very few time points for each subject. They call the time points waves. For instance, it could be medical study where each patient has 4-5 observations at monthly intervals, and hundreds of patients over the course of ...
In both cases, do retrain on the entire data set, including the 90s days validation set, after doing your initial train/validation split. For statistical methods, use a simple time series train/test split for some initial validations and proofs of concept, but don't bother with CV for Hyperparameter tuning.
Scenario building. Forecast by analogy. Executive opinion. One of the best methods that I know that works very well is the use of structured analogies (5th in the list above) where you look for similar/analogous products in the category that you are trying to forecast and use them to forecast short term forecasting.
Background: I'm modeling a time series of 6 year (with semi-markov chain), with a data sample every 5 min. To compare several models, I'm using a 6-fold cross-validation by separating the data in 6 year, so my training sets (to calculate the parameters) have a length of 5 years, and the test sets have a length of 1 year.
132. $\begingroup$. Pearson correlation is used to look at correlation between series ... but being time series the correlation is looked at across different lags -- the cross-correlation function. The cross-correlation is impacted by dependence within-series, so in many cases $^ {\dagger}$ the within-series dependence should be removed first.
If your data is quarterly: dummy Q2 is 1 if this is the second quarter, else 0 dummy Q3 is 1 if this is the third quarter, else 0 dummy Q4 is 1 if this is the fourth quarter, else 0 Note quarter 1 is the base case (all 3 dummies zero) You might want to also check out "time series decomposition" in Minitab -- often called "classical decomposition".
May 18, 2018 at 10:55. One of the beautiful things about the R software is that, with a moderate amount of programming work up front, it enables one to test for trend and test for seasonality via autocorrelation, and to do this for 10,000 different products, saving the info on products that have a result that meets a certain threshold ...