Unlock the Power of Neural Networks for Time Series Forecasting
When it comes to forecasting multiple time series, the complexity can be overwhelming. Traditional methods often fall short, but with Neural Networks, a whole new world of possibilities opens up. Neural Networks can handle multiple time series with ease, offering flexibility and accuracy like never before.
As an added bonus, Neural Networks make it simple to incorporate exogenous variables into your forecasts, and can accurately predict multiple time steps into the future. Say goodbye to the limitations of traditional methods and hello to a powerful solution that excels in a wide range of scenarios.
Interested in diving into Neural Networks for Time Series Forecasting? In this article, we’ll guide you through the process step-by-step, from transforming your data into a format suitable for training your models to making accurate multivariate forecasts with Deep Neural Networks and LSTMs.
Examining Our Data
Let’s kick things off by exploring a dataset that captures daily mean temperature and humidity in Delhi, India from 2013 to 2016. This dataset is readily available on Kaggle and is licensed under CC0: Public Domain, making it a perfect choice for our exploration.