SaLLy - Time Series Forecasting
Time series forecasting is a subfield of data analysis and predictive modeling that focuses on predicting future values of a time-dependent dataset based on historical patterns and trends. Time series data consists of observations collected sequentially over time, such as stock prices, weather patterns, or sales figures. Time series forecasting involves developing mathematical models and statistical techniques that can capture the complex patterns in the data and use them to make accurate predictions about future values. These predictions can be used for a variety of applications, including business forecasting, financial modeling, weather forecasting, and more. Time series forecasting models can range from simple statistical models to complex machine learning algorithms, and the choice of model depends on the nature and complexity of the data and the specific application.
In this topic, we are interested in developing and using a wide range of techniques for time series analysis and forecasting to transform data into information and decision-making.
Meet Our Time Series Forecasting Team
Javier Linkolk López
Research Coordinator & Team Leader
Key Publications
- da Silva, K.L.S., López-Gonzales, J.L., Turpo-Chaparro, J.E., Tocto-Cano, E., and Rodrigues, P.C. (2023). Spatio-temporal visualization and forecasting of PM10 in the Brazilian state of Minas Gerais. Scientific Reports, 13, 3269.
- Iftikhar, H., Bibi, N. Rodrigues, P.C., and López-Gonzales, J.L. (2023). Multiple novel decomposition techniques for time series forecasting: Application to monthly forecasting of electricity consumption in Pakistan. Energies, 16, 2579.
- Kazemi, M. and Rodrigues, P.C. (2023). Robust singular spectrum analysis: Comparison between classic and robust approaches for model fit and forecasting. Computational Statistics. DOI: 10.1007/s00180-022-01322-4
- Mesquita, V.B., Oliveira Filho, F.M. and Rodrigues, P.C. (2021). Detection of crossover points in detrended fluctuation analysis: An application to EEG signals of patients with epilepsy. Bioinformatics. 37, 1278-1284. DOI: 10.1093/bioinformatics/btaa955
- Sulandari, W., Subanar, Lee, M.H. and Rodrigues, P.C. (2020). Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy. 190:116408. DOI: 10.1016/j.energy.2019.116408