Is this the only way to handle time series data?

External question — Data Science Stack Exchange Author: nullUser Original post: https://datascience.stackexchange.com/questions/111864 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Let's assume I have recorded data from 1/1/2000 to 30/12/2018 it could be stock price dataset or rate (interest, currency or employment..etc) And I want to predict price/rate for given date (1/1/2015 - 30/12/2018). I read a few papers and checked some examples and I noticed almost all of them just use 2 variables Date & target variable(the variable that we want to predict pric/rate) concerted to time series. My question is, is this the only approach? What about the other variables won't they affect my prediction? When it comes to these cases (dealing with time series), how can I use the other variables to predict rate/price ?

External answer — Data Science Stack Exchange Author: Hetarth Chopra Original post: https://datascience.stackexchange.com/a/111871 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Most of these self-proclaimed data science tutorials on stock market price prediction are wrongly taught. I recently found this out using this video on youtube -> https://www.youtube.com/watch?v=xOcyV5Q2G5I . There are far more important variables that are needed to predict stock price than the previous price lol, for example - volume traded etc.