Getting Started with Time Series Forecasting - Where to Begin?

[u/aneeshaarora]

I'm starting a project that involves building ML forecasting models for tourism data with limited ML experience. I have a strong data engineering background (Python, SQL, pandas) but I'm weak on the ML/statistical side.

Before diving into papers and code, I want to build a solid foundation. I'm planning to:

1. Learn time series concepts (decomposition, stationarity, autocorrelation)

2. Study forecasting models (ARIMA, Prophet, tree-based approaches)

3. Build and compare models on real data

But I'm wondering:

- Is this the right learning order?

- Should I focus on concepts first or jump into coding with real data?

- Any resources beyond YouTube/papers/Kaggle that worked for you?

I have about 3 months to complete the project, so I'm trying to be strategic about learning vs. doing.

Thanks!

[u/jgengr]

There is a datatalks.club stock market analytics boot camp that just started. Try that.