Predicting time series quantities has been an interesting domain in predictive analytics. Many traditional Quantitative forecasting methods like ARIMA, SES or SMA are time tested and widely used for things like predicting stock prices. With the amount of data we have around, neural networks make a good candidate for time-series forecasting that can outshine traditional statistical methods. Over the past few years, we had many new neural network architectures like CNN, RNN, and LSTM making inroads into Deep Learning.

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The short time I have spent on Kaggle, I have realized ensembling (stacking models) is the best way to perform well. Well, I am not the only one to think so !! Stacking is a Model Ensembling technique that combines predictions from multiple models and generates a new model. I am gonna write a new post on model ensembling 🙂 I have experimented with multiple ensembling techniques and made a model with XGboost, LightGBM, and Keras for Zillow Zestimate problem which did perform well.

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