Mexico is the world’s largest consumer of eggs, producing 3.05 million Mg in 2021. The high variation in wholesale prices is a feature of the egg production system, which is important to producers ...and government institutions that need to forecast future prices for activity planning. As a result, it is necessary to propose tools that can reliably predict egg prices. The goal of this paper was to compare the performance of various statistical models by analyzing the time series of egg prices using the Akaike index and forecast error to determine which model best predicts the wholesale price of white eggs. The models evaluated were the autoregressive integrated moving average model (ARIMA), ARIMA with interventions, ARIMA with transfers, and regression with ARIMA errors. Two time series were used: the wholesale price of white eggs, constructed with data from the National System of Information and Market Integration (SNIIM) and the Agrifood and Fisheries Information Service (SIAP), and egg imports, calculated with data from the Economic Information System. The latter was used as an exogenous variable to explain the price of eggs. Both cover the period from January 2006 to December 2021. According to the Akaike index, the model with the best adjustment was ARIMA (0,1,1)(1,0,1)12 with interventions. In the evaluation of forecast error, the best models were the regression models with ARIMA (1,1,0)(1,0,1)12 and ARIMA (1,1,0)(1,0,1)12 errors with transfer.
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using ...several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background: Tuberculosis in India is a major public health problem. The National Strategic Plan 2017-2025 aims to achieve a rapid decline in burden of TB, morbidity and mortality while working ...towards elimination of TB in India by 2025. By proper care and treatment of TB patients, the battle against TB can be won. In the current scenario, forecasting of Tuberculosis incidence and annual case notification rate could help policy makers in planning an effective intervention at the right time keeping in mind the target for TB elimination. Objectives: 1) To study the trend of Tuberculosis in India. 2) To forecast the incidence and annual case notification rate due to Tuberculosis in India for next 3 years. Methodology: Data regarding the incidence and annual case notification rate were collected from the annual reports of Tuberculosis published by Central TB Division, Government of India. Data from the sources were collected in a data extraction sheet and entered in MS excel. Gretl software was used for data analysis. Autoregressive Integrated Moving Average (ARIMA) method was used to select the best fitted model for available time series data and using the selected model incidence and annual case notification rate were forecasted for next 3 years. Results: The study showed an upward trend in incidence and annual case notification rate in the sampling and post sampling period. According to the forecast, for the year 2025 the incidence of Tuberculosis will be 28.36 lakhs (95%CI- 26.4886, 32.8556) and annual case notification rate will be 178 (95%CI- 144, 211). Conclusion: There is an upward trend of Tuberculosis incidence and annual case notification rate in India in forecasted period. To achieve the set targets, the policy makers need to plan and implement more effective interventions at the right time.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK, VSZLJ
Abstract
Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and ...LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.
Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. Traffic flow prediction is an important aspect in Intelligent Transport Management System ...(ITMS). In this context, one of the most in-demand techniques of Machine Learning, especially Time series based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. Our aim is to perform computations related to traffic on the traffic datasets and find out the accuracy for each model. For this purpose we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves highest prediction accuracy with the lowest root mean squared error.
Detection of outliers in ECG signal Socha, Maciej; Duraj, Agnieszka; Szczepaniak, Piotr S.
Procedia computer science,
2023, 2023-00-00, Volume:
225
Journal Article
Peer reviewed
Open access
The article concerns the detection of outliers in the ECG signal. Premature ventricular and supraventricular beats are treated as outliers. Our solution is to analyze the distance between successive ...QRS complexes using the ARIMA model. The developed solution and performed tests confirm the correct search for outliers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Real estate is a favored investment option as it allows investors to diversify their portfolios and minimize risk. Investors can invest in real estate directly by purchasing a property, or through ...real estate investment funds (REITs) where they can purchase shares in companies that own and manage real estate. Investing in REITs has become increasingly popular because it eliminates some of the disadvantages associated with direct real estate investment, such as the need for a large upfront payment. When investing in mixed asset portfolios, it is crucial to predict future prices accurately to ensure profitable and less risky asset allocation. However, literature on price prediction often focuses on only one or two algorithms, and there is no research that explores REITs’ price prediction in the context of portfolio optimization. To address this gap, we conducted a thorough evaluation of 5 machine learning algorithms (ML), including Ordinary Least Squares Linear Regression (LR), Support Vector Regression (SVR), k-Nearest Neighbors Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long/Short-Term Memory Neural Networks (LSTM), as well as other financial benchmarks like Holt’s Exponential Smoothing (HES), Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and Auto-Regression Integrated Moving Average (ARIMA). We applied these algorithms to predict future prices for 30 REITs from the US, UK, and Australia, as well as 30 stocks and 30 bonds. The assets were then used as part of a portfolio, which we optimized using a genetic algorithm. Our results showed that using ML algorithms for price prediction provided at least three times the return over benchmark models and reduced risk by almost two-fold. For REITs, we observed that the use of ML algorithms led to a higher allocation to REITs diversified by country. In particular, our results showed that SVR was the best-performing algorithm in terms of risk-adjusted returns across different time horizons, as confirmed by our Friedman test results (Sharpe ratio). Overall, our study highlights the effectiveness of ML algorithms in predicting asset prices and optimizing portfolio allocation.
•ML comparison for accurate prediction of real estate, stock, and bond time series.•Genetic algorithm to optimize prediction-based multi-asset portfolio including REITs.•Comparison against econometric benchmarks and historical data approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Este estudo buscou selecionar um modelo para a previsão dos alertas de desmatamento na Amazônia legal a partir dos dados gerados pelo monitoramento via satélite do DETER-B, entre agosto de 2015 e ...abril de 2022. A série temporal de alertas de desmatamento foi analisada e, em seguida, realizaram-se previsões dos alertas de desmatamento, se valendo dos modelos de previsão da classe ARIMA sazonais. Foi identificada a presença de quebra estrutural em maio de 2019 e sazonalidade estocástica. Foram feitas previsões dinâmicas para seis períodos a frente do último valor da amostra, para comparar com os valores de fora da amostra e verificar a qualidade das previsões. As especificações foram precisas em prever os alertas seis meses à frente, indicando que os formuladores de políticas públicas podem criar expectativas razoáveis quanto aos alertas, principalmente, ao permitir a adoção de medidas de cunho preventivo ao desmatamento.
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