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What is the binary time series in Excel?
I have a binary time series: We have 2160 data (0=didn’t happen, 1=happened) for one-hour period in 90 days. I want to forecast after these 90 days, where the next 1 will happen, and also Extend this provision for next one month.
How to statistically compare two time series?
This common model could be estimated globally and separately for each of the two series and then one could construct an F test to test the hypothesis of a common set of parameters. Consider the grangertest () in the lmtest library. It is a test to see if one time series is useful in forecasting another. Just came across this.
Is there a good correlation between two series?
If the r is small your conclusion would be that they are weakly related and so no desirable comparisons and a larger value if r would suggest good comparisons s between the two series. The third step where there is good correlation is to test the statistical significance of the r.
Which is the best model to describe each series separately?
With that in place I would identify a common model that would reasonably describe each series separately. This might be an ARIMA model or a multiply-trended Regression Model with possible Level Shifts or a composite model integrating both memory (ARIMA) and dummy variables.
When to use hold out cross validation for time series data?
So, rather than use k -fold cross-validation, for time series data we utilize hold-out cross-validation where a subset of the data ( split temporally) is reserved for validating the model performance. For example, see Figure 1 where the test set data comes chronologically after the training set.
What should be done with time series data?
With time series data, particular care must be taken in splitting the data in order to prevent data leakage.
How to split time series without causing data leakage?
We discussed how to split time series data without causing data leakage, specifically suggesting two methods: 1) Predict Second Half and 2) Day Forward-Chaining. Then, we addressed how to handle multiple independent time series using two methods: 1) regular and 2) population-informed.