ARIMA model detects credit card fraud in unbalanced datasets.
problem Unsupervised credit card fraud detection in unbalanced datasets.
method ARIMA model applied to customer spending patterns for anomaly detection.
result ARIMA model outperforms benchmark anomaly detection methods.
Combines spline interpolation and ARIMA for stock market forecasting.
problem Limited predictive performance of ARIMA in noisy data.
method Integrates cubic spline interpolation and ARIMA for time series forecasting.
result Demonstrates guidance for short-term stock market forecasting.
Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks. RNNs are competent in understanding temporal dependencies. The use of LSTM cells fu…
ARIMA and LSTM models predict stock prices with varying accuracy.
problem Predicting stock prices accurately.
method ARIMA and LSTM models trained on historical stock data.
result LSTM model performs better in predicting stock price changes.
Hybrid LSTM-ARIMA model outperforms other algorithms in algorithmic investment strategies.
problem Developing an optimal algorithmic investment strategy.
method Combines LSTM and ARIMA models, using LSTM for final predictions and ARIMA residuals for boosting.
result LSTM-ARIMA algorithm outperforms all other algorithms across multiple equity indices.
New ARIMA framework improves forecast accuracy for economic and financial time series.
problem Improving forecast accuracy for nonlinear dynamics in time series data.
method Projection-based ARIMA framework using Galerkin basis expansions.
result Galerkin-SARIMA matches or improves forecast accuracy compared to classical ARIMA/SARIMA.
New approach improves convergence of online learning for ARIMA models.
problem Pitfalls in computational complexity and convergence of Online Newton Step optimization method.
method Proposes a new approach combining different Online Gradient Descent learners for iterative training of ARIMA models.
result The proposed approach outperforms existing methods resulting in lower prediction error.
This paper improves fund net value prediction using ARIMA-LSTM hybrid model.
problem Inaccurate prediction of fund net value due to linear characteristics of models.
method ARIMA-LSTM hybrid model: ARIMA for linear characteristics, LSTM for nonlinear.
result Hybrid model shows higher accuracy in predicting fund net value.
Study compares ARIMA, ETS, NNAR and hybrids for COVID-19 hospitalizations.
problem Forecasting the second wave of COVID-19 hospitalizations in Italy.
method ARIMA, ETS, NNAR, and hybrid models.
result Hybrid models outperform single models in capturing epidemic patterns.
Advanced forecasting models outperform Holt-Winters and ARIMA for stock market data.
problem Forecasting stock market data with improved accuracy.
method Developed 24 two-parameter families of forecasting functions using a nonparametric approach.
result Our models outperform Holt-Winters and ARIMA in terms of lower sum of absolute errors and higher number of accurate forecasts.
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 series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Auto…
LSTM models outperform traditional ARIMA in S&P 500 forecasting.
problem Forecasting volatile financial data with non-linear dependencies.
method Compared LSTM and ARIMA models using historical data and technical indicators.
result LSTM models outperformed ARIMA in accuracy and error metrics.
Research predicts rice prices in Banda Aceh post-COVID using ARIMA models.
problem Forecasting rice prices in Banda Aceh post-COVID-19.
method Used LOCF imputation for missing data and auto-ARIMA for forecasting.
result ARIMA model (0,0,5) best for all rice qualities, showing price decline and then stability.
An econometric analysis of CRIX family indices.
problem Understanding the dynamics of CRIX family indices for pricing.
method Time-series econometric analysis using ARIMA and GARCH models.
result ARIMA(2,0,2)-t-GARCH(1,1) model captures volatility clustering and fat-tails.
Paper forecasts recession indicators using yield spread models.
problem Forecasting the leading indicator of a recession using yield spread.
method Applied econometric time series and machine learning models to forecast yield spread.
result Parsimonious univariate ARIMA model outperforms richly parameterized VAR method.
Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long…
This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same…
This research optimizes energy consumption forecasting in Puno using parallel computing and ARIMA models.
problem Improving energy consumption forecasting accuracy and efficiency in Puno.
method Parallel computing and ARIMA models for forecasting energy consumption.
result Notable improvements in computational efficiency and data processing capabilities.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
problem Forecasting volatile retail sales trends with unknown factors.
method Benchmarked traditional ARIMA model against advanced models like Prophet and LightGBM on historical Walmart sales data.
result ARIMA model outperforms LightGBM and achieves computational efficiency.
Classical time series models forecast Bitcoin prices and volatility accurately.
problem Forecasting Bitcoin prices and volatility using classical models.
method ARIMA, SARIMA, GARCH, and EGARCH models were trained and tested on Bitcoin price data.
result ARIMA models performed best for short-term price dynamics, while EGARCH models were best for volatility.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Tech sector decouples from non-tech sectors post-2015, predicting economic growth.
problem Understanding the relationship between technology and economic growth.
method ARIMA modeling, stationarity tests, data wrangling, exploratory data analysis.
result The technology sector decouples from non-technology sectors post-2015 and predicts economic growth.
Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer…
Paper uses LSTM neural networks to forecast commodity prices.
problem Forecasting accuracy of traditional methods like ARIMA.
method Long Short-Term Memory (LSTM) neural networks complement traditional methods.
result Forecast averaging of LSTM and ARIMA models improves forecast accuracy.
Study compares forecasting models for European financial markets and cryptocurrencies, finding hybrid ETS-ANN model best.
problem Challenges in predicting financial market fluctuations and cryptocurrency prices.
method Comparative analysis of ARIMA, hybrid ETS-ANN, and kNN models on European financial markets and cryptocurrency data.
result Hybrid ETS-ANN model performs best over extended periods, with moderate accuracy.
Unified framework evaluates synthetic financial data models.
problem Data scarcity and confidentiality in finance hinder model development and testing.
method Multi-criteria evaluation of three generative paradigms: ARIMA-GARCH, VAEs, and TimeGAN.
result TimeGAN achieves the best balance between realism and temporal coherence.
Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a …
Regulated Bitcoin futures led to higher volatility and trading volume.
problem Estimating the impact of regulated Bitcoin futures on volatility and volume.
method Employed a new causal approach, C-ARIMA.
result Regulated Bitcoin futures increased Bitcoin volatility by more than double.
Research combines econometric, machine learning, and deep learning models for financial forecasting.
problem Improving financial time series forecasting accuracy.
method Hybrid models combining ARIMA, SVM, XGBoost, and LSTM.
result Effective hybrid models outperform individual components and the Buy&Hold strategy.
Study combines sentiment analysis with traditional models for better S&P 500 trading.
problem Improving trading performance in volatile markets.
method Sentiment analysis from financial news, GPT-2, FinBERT, combined with technical indicators and time-series models.
result Combining sentiment-driven insights with traditional models improves trading performance.
Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…
An image-driven approach for time series forecasting.
problem Time-series forecasting as a computer vision task.
method Capture input data as an image and train a model to produce the subsequent image.
result Our method outperforms various baselines, including ARIMA, using image-based evaluation metrics.
Paper presents forecasting models for platelet demand.
problem Managing platelet demand and supply is challenging due to variability and short shelf life.
method Utilized ARIMA, Prophet, lasso regression, and LSTM networks on a clinical dataset.
result Multivariate approaches generally have higher accuracy, but simpler ARIMA can suffice with sufficient data.
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 results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such a…
The paper predicts Bitcoin prices using machine learning and sentiment analysis.
problem Predicting the future price of Bitcoin in USD.
method Applied supervised machine learning and sentiment analysis to Twitter and Reddit data.
result LSTM models with multi-feature analysis outperformed ARIMA models in predicting Bitcoin prices.
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as ETS and ARIMA gain their popularity not only from their high accuracy, but they are also suitable for non-expert users as…
PARNN improves ARNN with ARIMA feedback for accurate long-range forecasting.
problem Accurate long-range forecasting of complex time series data.
method Improves ARNN using ARIMA feedback, providing uncertainty quantification.
result PARNN outperforms state-of-the-art forecasters across various horizons.
This paper predicts stock prices during unusual events like the pandemic.
problem Lack of models to predict stock price changes during catastrophic events.
method ARIMA, LSTM, sentiment analysis models trained on historical data.
result Achieved 98% prediction accuracy for stock prices during anomalous circumstances.
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear mo…
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to con…
Study models live cattle futures prices in Brazil.
problem Estimate futures prices for better risk management.
method Holt-Winters, ARIMA, GARMA models.
result GARMA(2,1) model best predicts BGI prices.
Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retro…
Time series analysis is a key component of machine learning, with applications in various fields.
problem Time series analysis in machine learning
method Basic concepts, classical statistical models, modern machine learning approaches
result Machine learning techniques for time series analysis
This paper optimizes cryptocurrency portfolios by clustering price correlations and improving risk-return profiles.
problem Volatility and regulatory uncertainty in cryptocurrency markets make portfolio construction challenging.
method The paper combines network analysis, price forecasting, and portfolio theory to identify stable groups of correlated cryptocurrencies.
result Predictive consensus-clustering portfolios maintain positive and stable performance up to a 14-day horizon, with favourable gain-loss asymmetry and tighter tail-risk control.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
Study compares local and global models for hierarchical forecasting accuracy.
problem Challenges in hierarchical time series forecasting, especially in accuracy and information utilisation.
method Developed and evaluated local and global forecasting models (GFMs) to exploit cross-series and cross-hierarchies information.
result Global Forecasting Models (GFMs) outperform local models in hierarchical forecasting accuracy and computational efficiency.
MARS model outperforms others in stock price prediction across sectors.
problem Developing accurate models for stock price prediction.
method Used time series, econometric, machine learning, and deep learning models on stock data.
result MARS model is the best performing model across IT, Banking, and Health sectors.
Deep learning models outperform traditional methods in stock price prediction.
problem Improving stock price prediction accuracy using deep learning.
method Comparative analysis of deep learning models (LSTM, GRU) and traditional methods (ARIMA, ARMA) on historical data.
result Deep learning models, particularly LSTM, outperform traditional methods in predicting stock prices across different time horizons.