Abstract
A prominent area of data analytics is "time-series modeling" where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods.
The datasets this empirical research utilized were secondary quantitative of the real prices of the currently most used cryptocurrencies. We found that AutoML for time-series is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting time-series data.