How to deal with different amounts of data every day?

External question — Data Science Stack Exchange Author: user900476 Original post: https://datascience.stackexchange.com/questions/111743 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I am doing a time series prediction task. There are different amounts of news headlines every day, and the goal is a binary prediction task to predict next day's stock movement. The amount of headlines varies everyday. There might be 5 headlines, 6 headlines or more for one day. I am planning to embed each headline into a vector space of, for example, 300 dimensions. How shall I deal with it? As far as I know, neural networks require a fixed size of input. Should I pad my data? For example, there are at most 10 headlines everyday, so should I pad my data into size of [10, 300] for every day? PS: I don't want to compute the average of the embeddings because I want to know the impact of each news healine later.

External answer — Data Science Stack Exchange Author: alexmolas Original post: https://datascience.stackexchange.com/a/111744 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. One common option is to aggregate the embeddings of all the headlines. For example, you can compute the average of the embeddings and use the resulting 300-dim vector as input of the model.