# Sliding window approach using SVR & LightGBM

I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of each model. I've implemented the 1st model using Tensorflow 2.0. Training & testing data was created using tf.Dataset API. The data format is (window_data, forecast), where window_data represents a set of 24 timesteps and forecast represents the next timestep.

Now I need to train 2nd & 3rd model using SVR (LinearSVR to be more precise) and LightGBM. Is it possible to feed the model with a windowed dataset like in my 1st model?