A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting

Abstract

The term time series refers to historical data comprise of observations that are made in a fixed time step, successively, over a period of time. This work focuses on the training and application of modern Machine Learning approaches, like Deep Neural Network techniques, to model and predict general time series obtained from several open databases. The selection of the data that were used in the experiments was focused on specific economic and social phenomena, intending to predict their evolution over time. The main and final goal remains the comparison of the aforementioned predicting approaches, as well as the optimization of them in order to improve their accuracy.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 53 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.