I am new to time series forecasting and all the articles that I read online talk about time series forecasting using a variable that is tracked over time and then forecasted after the model is trained. These examples include - sensor readings over time, daily stock price movements, temperature swings etc.
What I am looking to learn is - how can we include other predictors/independent variables in time series forecasting? Comparing it with linear regression or supervised problem framing in general, for instance, we can have one hot encoded variables as predictors in time-series model.fit() ? How does this thing work?
Since I am new to this time series forecasting domain, I would appreciate if someone can point me to right resources/links/blogs. Thanks in advance.
Here is the problem that I am facing in the marketing domain - given some historic data about how marketing leads are generated, I want to forecast how many leads would be created in next 2/3 quarters. So this is not a traditional linear regression problem where I have X variables and I try to predict my target variable y which is #LeadsGenerated in this case. Neither this is a straight forward Time Series forecasting problem where I have some temporal features that I can fit a model upon.
What business needs to understand is - given the historical data (like marketing spend data, campaign performance data, news articles etc) for last 3 years, how many leads will be generated in future?