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In addition, we introduce random temporal shifts for each trend time series to test the robustness of the algorithms.
We evaluate the robustness of the algorithms by introducing random temporal shifts on the trend time series.
In addition, these models are suitable for forecasting stationary or trend time series, but they are not appropriate for forecasting seasonal time series.
Then, DTW computes the distance between all pairs of points of two given trend time series (f^{i}_{k}) and (f^{j}_{k}).
To asses a time trend, time was admitted as a continuous variable.
After controlling for the covariates, we found that the results were robust in all three model specifications regarding whether and how secular trend (time) was included in the model.
Similar(53)
We can also determine the expected duration of trend times stemming from the impact of active-ratio.
Observed and adjusted trends (time modelling) Fig. 138 (abstract A1153).
The straight line suggests an exponential family of the trending time distribution.
The majority of tweets are observed after the trending point, with a rapid increase around trending time.
Here's a transcript of our conversation on social media trends, time management, Vayner Media, startups and more.
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