Our dataset contains a multi-variate timeseries data for Median Price (MA) in quarterly intervals. A multivariate time series has more than one time-dependent variable. Each variable depends not only on its past values but also has some dependency on other variables.
Refer to Kaggle for data set and starting Kernel: [login to view URL]
Objective: forecast MA for 8 future quarters using a model with accuracy that exceeds the current MAPE baseline in Kaggle notebook, without over-fitting the model!
Try 3 different models and pick one that yields the best results i.e. models from VARMAX class (VARMA, VMA), Multi-variate regression, LSTM, MLP, RNN or any other model of your choice.
1. Every model needs to be in a separate notebook with comments explaining as to what is being done. Minimum 1 comment per 3 lines of code.
2. The forecast should be done on the 90% train and validated against the 10% as per Kaggle notebook.
3. The train forecast should be compared with test set and MAPE values presented for every model must be below those of the VAR baseline set in Kaggle notebook.
4. Test set should be witheld from the model and only be used to produce the MAPE scores.
5. At least 3 different algorithms will be tried. It is ok to include a variant of the Kaggle VAR model or improve the VAR parameters in the Kaggle notebook in one instance of the three.
6. At least one of the models will use the [login to view URL] dataset.
7. Forecasts produced from the raw dataset should be equivalent to those seen in the Kaggle notebook i.e. medianed quarterly intervals that should be compared against the test set as per point 3 above.
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