How are multivariate time series models a problem?
Multivariate time-series models involve a large number of unknown parameters, a problem which is greatly exacerbated when nonlinearities are introduced. Conceptually, the extension of univariate nonlinear models to the multivariate setting is straightforward.
Is it possible to model time series data y?
However, under a special circumstance, we can model time series data y using time series data x, when x and y are both I (1) process and cointegrated. Basically, cointegration means there is an amplifying effect in between x and y.
How to analyse multiple time series variables in Python?
To model time series data y using time series data x, we usually require both the x and y to be stationary I (0) process. If you are not familiar with stationarity test of a single time series variable, please refer to my previous post: Time Series Modeling With Python Code: How To Analyse A Single Time Series Variable.
What causes strong correlation between two time series variables?
This strong correlation may be purely caused by the fact that the two time series variables have non-constant mean. This phenomenon is called spurious relationship. However, under a special circumstance, we can model time series data y using time series data x, when x and y are both I (1) process and cointegrated.
Can a time series model be used in real life?
But even a time series model has different facets. Most of the examples we see on the web deal with univariate time series. Unfortunately, real-world use cases don’t work like that. There are multiple variables at play, and handling all of them at the same time is where a data scientist will earn his worth.
How to predict multiple time series at once?
Here I will demonstrate how to train a single model to forecast multiple time series at the same time. This technique usually creates powerful models that help teams win machine learning competitions and can be used in your project. And you don’t need deep learning models to do that! In machine learning, more data usually means better predictions.
How to create time series with multiple seasonal periods?
For example, if there are ‘M’ periods (p 1, p 2, p 3, p M) in the data, we would have different fourier series corresponding to each of the ‘M’ periods. In this particular example, there are two seasonal periods, daily and weekly, with p 1 including 24 hours and p2 including 168 hours.