# Forecasting Multivariate Time Series
## 4 Conclusions and Discussion  
Time series analysis has grown increasingly intricate due to the multitude of techniques available. Choosing the appropriate forecasting method and preprocessing strategy based on the characteristics of the time series is crucial for obtaining reliable results. Proper steps must be followed in time series analysis to identify the best models applicable in practical settings. Statistical and machine learning models, along with various imputation techniques for handling missing data, offer diverse options for analysis. With abundant research detailing each technique's performance on specific datasets, there is a need for a system capable of efficiently applying these techniques to identify the best model through optimization. Through experiments on various time series datasets, the efficacy of the proposed framework, Preptimize, is evident. Preptimize recommends multiple blueprints for prediction models, serving as a preliminary step towards analyzing the given time series and saving significant time and effort. Researchers can further enhance these models by creating hybrid combinations, tuning parameters, and adding more features to improve forecasting accuracy. Preptimize generates detailed reports covering all data processing steps, along with charts, graphs, statistics, and a comprehensive evaluation sheet showcasing the performance of all models tested on the dataset using different error metrics. Different experiments were conducted on various datasets to evaluate the performance of this framework. The effectiveness of the framework can be judged by how closely the blueprint model matches those used in the literature and by comparing the accuracy based on RMSE. Benchmark performances for stock market prediction models are based on LSTM, SVR, and hybrid models combining statistical methods with machine learning techniques, often reporting RMSE values as low as 0.02 for stock prices. Preptimize also suggested LSTM and ARIMA for stock price forecasting. Benchmark performances for Microsoft (MSFT) stock prediction reported an RMSE of 0.05 with a hybrid model combining LSTM and RNN [45]. The blueprint model proposed with Preptimize achieved an RMSE of 0.029 using Vanilla LSTM. Bitcoin's high volatility, with significant price fluctuations over short periods, makes it challenging for traditional forecasting models. Bitcoin price prediction reported RMSE values ranging from 4 to 7 for LSTM and random forest models. Preptimize suggested LSTM (RMSE 10.02) for Bitcoin prediction, aligning with findings in the literature. A study [46] reviewed various forecasting models, including feedforward neural networks, LSTM, GRU, ARIMA, and AI techniques, on an individual household electric power consumption dataset to compare their performance. The average RMSE across 12 different models was 0.83, with the lowest RMSE of 0.75 achieved by both LSTM and GRU. In contrast, Preptimize recommended using GRU preprocessed by exponential smoothing, which resulted in an RMSE of 0.639. Based on the discussion, we can conclude that Preptimize effectively recommends suitable prediction models for a given dataset, facilitating rapid decision making with minimal effort. Preptimize has certain limitations that should be acknowledged. Firstly, it is resource-intensive, requiring significant computational power and time due to its comprehensive approach of applying numerous techniques on a data sample to identify the optimal preprocessing and forecasting methods. Secondly, Preptimize primarily supports datasets with a normal distribution. When dealing with non-normal distributions, it transforms the series to approximate normality, which can add complexity and potential inaccuracies, especially if the transformation does not fully achieve a normal distribution. We aim to address these in future work. While the proposed framework focuses on addressing the missing data problem in time series analysis, numerous other challenges such as outlier detection, concept drift, and heteroscedasticity persist.