[SOLVED] CSE510-Project 2 Time-series Prediction

Description

This project is focused on forecasting a time-series data.

Tasks

Part I: Prepare the dataset for training

1. Choose the dataset
2. Extract and describe the main statistics about the dataset and provide visual representation of the dataset.
3. Preprocess the dataset for training (e.g. cleaning and filling the missing variables, split between training/testing/validation)

Part II: Classical time series forecasting methods

1. Choose the features and targets in the dataset.
2. Apply statistical algorithms (min 3 algorithms) to forecast the values on different setups (min 3 different setups). Possible algorithms include: ARIMA, VAR, SARIMAX, etc.
3. Provide the comparison of the results of different statistical models you have used. This can be in the form of graph representation and your reasoning about the results.

Part III: Deep learning time series forecasting methods

1. Apply MLP to predict the value. Show the results on 3 different MLP setups

(#layers, activation functions, learning rate, layers structures, etc)

2. Apply RNN or LSTM architecture to predict the value.
3. Plot the graphs (predicted vs true values, accuracy, loss)
4. Discuss and provide the results of predicting the values using different deep learning structures.