EasyTime: Time Series Forecasting Made Easy

In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR---ANFIS). AR---ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR---ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR---ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and the absolute mean square error were also used to measure the accuracy of the estimation for methods used. The important result obtained in this paper is that the optimal neural network was the Backpropagation (BP) and Recurrent neural networks (RNN) to solve time series, whether linear, semilinear, or non-linear. Besides, the result proved that the inefficiency and inaccuracy (failure) of RBF in solving nonlinear time series. However, RBF shows good efficiency in the case of linear or semi-linear time series only. It overcomes the problem of local minimum. The results showed improvements in the modern methods for time series forecasting.

Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach ∼.

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

Time series forecasting have attracted a great deal of attention from various research communities. There are many methods which divide time series into subseries. Information granules, fuzzy clustering and data segmentation are among the most popular methods in this field. However all these methods are designed to recognize dependencies between adjacent points. In order to do so, they divide the time series into time intervals. This imply some limitations in findings strongly non-local dependencies between points scatter across whole time series. The Divide and Conquer ensemble algorithm here presented was designed to address such limitations. The model samples the series into many subseries, searches for possible patterns and finally chooses the most significant subseries for further investigation. Since the prediction error evaluated on the subseries is lower than the one evaluated on the original time-series, the proposed strategy can significantly mitigate the overall prediction error. In order to evaluate the efficiency of our approach we performed the analysis on various artificial datasets. In a real world example our algorithm showed a 3-fold improvement of the accuracy with respect to other state-of-the-art methods. Although the algorithm was designed for time-series forecasting, it can be easily used for noise filtering purposes. Simulations reported in the present work illustrate the potential of the method in this field of application.

Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

Background: One forecasting method that is often used is time series forecasting. The development of applied mathematics has encouraged new mathematical findings that led to the birth of new branches of mathematics, one of which is fuzzy. Purpose: The objectives of the study, namely forecasting, fuzzy set, time series, fuzzy time series, fuzzy time series Singh, interval ratio and measurement of accuracy level. Method: This research method applies Chen's fuzzy time series in the section of determining the universe of talk you to the fuzzification of historical data and in the part of forecasting results obtained through a heuristic approach by building three forecasting rules, namely Rule 2.1, Rule 2.2, and Rule 2.3 to obtain better results and affect very small AFER values. As well as making modifications to the interval partition section using interval ratios to be able to reflect data variations. Results: Based on the calculation of AFER values for order 2, order 3, and order 4 respectively obtained at 1.06389%, 0.689368%, and 0.711947%. Therefore, it can be said, Singh's fuzzy time series forecasting method based on the ratio of 3rd-order intervals is better than that of 2nd-order and 4th-order. Conclusion: Based on the results of research and discussion that has been carried out, it can be concluded that Singh's fuzzy time series forecasting method has the same algorithm as fuzzy time series forecasting. Singh's fuzzy time series forecasting method based on interval ratios applies fuzzy time series and Singh forecasting. Singh's fuzzy time series forecasting modification accuracy rate based on interval ratios produces excellent forecasting values according to evaluator average forecasting error rate (AFER).

In recent years Neural Networks and especially Deep Neural Networks (DNN) have seen a rise in popularity. DNNs have increased the overall performance of algorithms in applications such as image recognition and classification, 2D and 3D pose detection, natural language processing, and time series forecasting. Especially in image classification and recognition, so-called Convolutional Neural Network (CNN) have gained high interest as they can reach a high accuracy, which makes them a viable solution in cancer detection or autonomous driving. As CNNs are widely used in image tasks, different visualizations techniques have bee developed to show how their internals are working. Apart from image tasks, CNNs are applicable to other problems, e.g., time series classification, time series forecasting, or natural language processing. CNNs in those contexts behave similarly, allowing for the same visualization techniques to make them more interpretable. In this chapter, we adapt image visualization algorithms to time series problems, allowing us to build granular, intuitively interpretable feature hierarchies to make a time series forecast as understandable as an image recognition task. We do so by using our previous work on power time series forecasting using CNN Auto Encoders (AEs) and applying typical CNN visualization techniques to it. Thus, we guid computer scientists to provide better interpretable figures for a time series forecasting task to application domain experts.KeywordsVisualizationConvolutional neural networksTime series data

В работе предложены математические модели прогнозирования нестационарных временных рядов с учётом внешних факторов и методы их структурной идентификации, основанные на совместном использовании многомерного варианта метода «Гусеница»-SSA и моделей SARIMAX, расширенных для прогнозирования временных рядов с несколькими сезонными составляющими и с учётом нескольких экзогенных переменных. Экспериментальные результаты показывают высокую эффективность предложенных моделей прогнозирования при выборе подходящих структурных параметров в сравнении с моделями SARIMAX.

Forecasting time series data is one of the most important subjects that is useful and applicable in real life. The objective of this study improves the performances of time series forecasting method called ARIMA with wavelet transform. The proposed method is taking an optimal type of Daubechies wavelet transform functions. Real case datasets in existing paper are used to compare the performance with original and existing forecasting methods. The results of experiment demonstrate the usefulness and superiority of the proposed method with more possibility.

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes five sections (1) data preprocessing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever‐growing demand for time series forecasts is automating this design process. The article, thus, reviews existing literature on automated time series forecasting pipelines and analyzes how the design process of forecasting models is currently automated. Thereby, we consider both automated machine learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we first present and compare the identified automation methods for each pipeline section. Second, we analyze these automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the reviewed literature that contributes toward automating the design process, identify problems, give recommendations, and suggest future research. This review reveals that the majority of the reviewed literature only covers two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large‐scale application of time series forecasting.This article is categorized under:Technologies > Machine LearningTechnologies > PredictionAlgorithmic Development > Spatial and Temporal Data Mining

Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of machine learning techniques, especially for boosting techniques and genetic programming. Boosting techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming (GP) and boosting technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional boosting algorithms, this new algorithm is called boosting using correlation coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.

While conventional models have already demonstrated exceptional performance in time series forecasting, several challenges remain to be tackled. The emergence of large language models (LLMs) has introduced disruptive changes to forecasting methodologies, including architectural frameworks and forecasting processes. To conclude existing literature regarding the application of LLMs in time series forecasting, this study, distinct from prior reviews, offers a comprehensive and up-to-date general pipeline block structure that integrates various modeling techniques in time series forecasting with LLMs, with specific emphasis on data scenario usage. To guide practical applications, this study proposes three collaborative patterns for incorporating LLMs in time series forecasting workflows and provides a comparison of LLMs’ performances with conventional models. Overall, this study organizes and evaluates the latest research advancements, delivering key insights and propositions for the potential future development of LLMs in the field of time series forecasting.

Accurate emergency call demand forecasting is essential for optimizing resource allocation and response times in Emergency Medical Services (EMS). Time series forecasting, a cornerstone of machine learning, plays a crucial role in predicting demand patterns. This research proposes a novel multi-series forecasting model for scenarios involving multiple independent time series, representing call data from distinct service areas. While previous research has explored multivariate time series and machine learning methods for EMS demand forecasting, this study focuses on comparing a simplified independent time series approach against the proposed multi-series model. By evaluating the accuracy of both methods in predicting future call volumes, the findings will provide insights into the most effective forecasting approach, ultimately contributing to improved resource allocation and enhanced patient care in EMS.