Leveraging Large Language Models for time series forecasting: A systematic literature review

A hybrid ARIMA and support vector machines model in stock price forecasting

A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

Problem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly at seasonal time series data. The second focused on the development of model building procedures of NN-MAR based on the properties of scale and wavelet coefficients. Then, the third was empirical study about the implementation of the proposed procedure and comparison study about the forecast accuracy of NN-MAR to other forecasting models. Results: The results showed that MODWT at seasonal time series data also has seasonal pattern for scale coefficient, whereas the wavelet coefficients are stationer. The result of model building procedure development yielded a new proposed procedure of NN-MAR model for seasonal time series forecasting. In general, this procedure accommodated input lags of scale and wavelet coefficients and other additional seasonal lags. In addition, the result showed that the proposed procedure works well for determining the best NN-MAR model for seasonal time series forecasting. Conclusion: The comparison study of forecast accuracy showed that the NN-MAR model yields better forecast than MAR and ARIMA models.

Credit Crisis and the Price of Gold: Evidence from a Forecast Based Modified Granger Causality Model

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network.

Objective: The article aims to explore and evaluate a novel deep-learning approach for time series forecasting using three specific models: SARIMA (Seasonal Auto-Regressive Integrated Moving Average), Neural Prophet, and Facebook Prophet. The primary goal is to assess the effectiveness of these models in predicting stock market values in the Gulf region, providing insights into the best-suited models for forecasting tasks. Methods: The study employs Python libraries and frameworks to implement the SARIMA, Neural Prophet, and Facebook Prophet models. The models are trained using stock market data from the Mulkia Gulf Real Estate dataset. The methodology includes data preprocessing, model training, evaluation, and comparison using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results: The evaluation results show that SARIMA performs well in general prediction tasks, especially when datasets contain seasonality trends. Facebook Prophet excels with smaller datasets containing seasonal data, while Neural Prophet demonstrates its ability to capture complex, non-linear patterns. However, Neural Prophet requires more intricate data and fine-tuning for optimal results. Contribution: This study provides a comparative analysis of deep-learning models for time series forecasting, offering valuable insights into their strengths and weaknesses. The findings contribute to the understanding of which models are most suitable for stock market prediction and how they can be adapted to different data types and scenarios. Conclusion: The research concludes that each model—SARIMA, Facebook Prophet, and Neural Prophet—has its unique strengths in time series forecasting. SARIMA is reliable for handling seasonal data, Facebook Prophet is efficient for smaller datasets with clear trends, and Neural Prophet is best for more complex datasets. The study highlights the importance of selecting the appropriate model based on the specific requirements of the forecasting task.

Accurate well rate forecasting is critical for field development. In the oil industry, recurrent-based deep learning models have been used for production forecasting. Modern research is shifting towards using the novel transformer architecture in natural language and time series applications to better handle long-term temporal dependencies using attention layers. In this paper, we present a novel approach of applying a transformer-based deep learning model for probabilistic production forecasting. The Temporal Fusion Transformer (TFT) model, a modern transformer-based model for time series forecasting, was used to provide a better oil rate prediction in the Norwegian Volve field. The historical bottomhole pressure, wellhead pressure, wellhead temperature and choke size opening real time series data were used as past input features in the TFT model to forecast ahead the oil rate of two wells. The model was trained with a quantile loss function to produce a probabilistic prediction with both upper and lower bounds of uncertainties. After optimization the TFT model was blindly tested on the last 20% of the data to evaluate its prediction performance compared Block Recurrent Neural Network architectures (BlockRNN). The real-time production data used in this multivariate forecasting problem was found to be complex with no clear trend or seasonality. The BlockRNN model failed to produce a good prediction of oil rate compared to the TFT model. The TFT model was better at encoding the input features including, the choke size, wellhead pressure, and temperature, and understanding their long-range dependencies with the oil rate.The model was able to minimize the testing Mean Squared Error (MSE) for the two wells F-11H and F-12H reaching values of 0.08 and 0.03, respectively. In addition, the model was able to forecast a prediction bandwidth in between the 10th and 90th quantiles to account for uncertainty ranges which laid in-between the blind test intervals. Overall, the TFT model was proven successful in accurately forecasting complex trends of oil rates overcoming the limitations of conventional, BlockRNN uses a memory to understand and make predictions about new data models of information loss over long-term multivariate time series prediction. Our work presents a novel approach of using the TFT probabilistic deep learning model for multivariate oil rate forecasting in the oil and gas industry. The model showed very promising results outperforming conventional BlockRNN-based models in addition to providing a range of the forecasting uncertainty using quantile regression. Knowing the uncertainty range helps in making critical decision particularly in the well intervention and field development.

Time series forecasting plays a critical role in decision-making across diverse domains such as finance, healthcare, and environmental monitoring. While classical statistical models like ARIMA remain interpretable and efficient, they often struggle with nonlinear patterns and dynamic dependencies. This review systematically examines how artificial intelligence (AI) and optimization techniques enhance forecasting accuracy and robustness. We evaluate modern deep learning architectures (e.g., LSTM, GRU, Transformers), hybrid frameworks (e.g., VMD-LSTM, CNN-GRU), and optimization-augmented models. A meta-analysis of over 150 studies reveals that deep learning-based approaches, particularly those enhanced with Adam and RMSProp optimizers, improve forecasting accuracy by up to 14% compared to traditional methods. Hybrid models demonstrate superior performance in multi-step predictions and handling volatility. The analysis includes financial datasets (S&P 500, NASDAQ) and environmental data (Beijing $$PM_{2.5}$$ ). Despite their power, AI-driven models face challenges including interpretability, computational cost, and data dependency. Future directions highlight explainable AI, transfer learning, and lightweight architectures to address these limitations. This work serves as a reference for researchers exploring the evolving landscape of time series forecasting through AI and optimization integration.

A new look at forecasting annual corporate earnings in the U.S.A.

In this study, composite earnings per share models are estimated for 35 chemical, food, and utility firms during the 1979-1980 period. It is generally held that financial analysts produce superior earnings forecast when compared to time series model forecasts, however, the results of this study indicate that the average mean square forecasting error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts. Moreover, despite the high degree o! correlation existing among analyst and time series forecasts, the ordinary least squares estimation of the composite earnings model is a better forecasting model than the composite earnings models estimated with ridge regression and latent root regression techniques. Standardization of regression variables also is addressed.

Optimization of the weights and asymmetric activation function family of neural network for time series forecasting

Recent time series forecasting developments indicated that simple linear models can be strong competitors to handle a vast majority of real-world datasets. However, the generality of such models can vary significantly between domains, not to mention between tasks. This paper extends the benchmark of Toner and Darlow Ordinary Least Squares (OLS) was investigated as a competitive baseline model for time series forecasting, to include a few more regression-based models. More specifically, we compare the performances of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree Regression (DT), and Random Forest Regression (RF) under the same experimental setup and datasets. Our experiments are performed using the ETTh1 benchmark dataset with fixed context horizon and forecasting horizon at 96-time intervals. Compared to the baseline OLS benchmark MSE of 0.376, and the corresponding Mean Absolute Error (MAE) of 0.399, our modified models clearly fare much better. Random Forest Regression does the best, at an MSE of 0.144, an MAE of 0.245, followed closely by Decision Tree Regression (MSE: 0.155, MAE: 0.262) and the KNN (MSE: 0.271, MAE: 0.369). Surprisingly, the modified version of the usual Gaussian Naive Bayes, which has been conditioned to regression using target binning , does an MSE of 1.053, with the corresponding MAE of 0.670, due to the probabilistic nature of the algorithm where the sample domain is not discrete but rather continuous. Our work highlights that Random Forests, which are tree models, are overwhelmingly powerful baselines, surpassing traditional linear methods. This demands a rethinking of the simplicity of definition of forecasting baselines and the use of more varied ML-based regressors as baselines. Source code and experimental configurations are released to facilitate reproduction and further extendibility of the code.

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems.