LLMs For Time Series Forecasting !!!
If you take a look at the industrial data you would see that in many places we are still using classical Machine Learning algorithms. There is a good reason to use classical ML and AI algorithms over new Deep learning-based methods in industrial settings; the amount and quality of proprietary data. Most banks still use some variant of XGBoost for tabular data. We have seen crazy progress in Deep Learning models, but there are still many fields where growth has been barely linear. One such field where we have seen limited growth is time series forecasting.
Topics Covered
- Understanding Time Series Data
- Turing Completeness
- Other Challenges And Approaches
- Foundational Models As Zero-Shot Forecasters (Chronos and TimesFM)
- Successor of LSTM: xLSTM
- Conclusion
Understanding Time Series Data
Time series data is one of the most naturally occurring forms of data. From internet usage to smart bands, from weather data to the stock market, everything comes under the umbrella of the Time series.
Time series is similar to predicting the future based on past events for a given number of quantities/attributes.
