96 citations · 186 across the 9 of their papers we have counts for
5 papers · 1 filter
In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models
Saroj Gopali, Bipin Chhetri, Deepika Giri +2
Existing data-driven approaches in modeling and predicting time series data include ARIMA (Autoregressive Integrated Moving Average), Transformer-based models, LSTM (Long Short-Ter…
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
Saroj Gopali, Faranak Abri, Sima Siami-Namini +1
There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning t…
Clustering Time Series Data through Autoencoder-based Deep Learning Models
Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah +2
Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data mining-based analysis r…
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM
Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate r…
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
Sima Siami-Namini, Akbar Siami Namin
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time s…