An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient

Розробка комплексної моделі інвестування на ринку криптовалют з використанням часових рядів та нейронних мереж

Дипломна робота: 88 c., 19 рис., 1 дод., 17 джерел. ФІЛЬТРАЦІЯ ДАНИХ, ЧАСОВІ РЯДИ, НЕЙРОННІ МЕРЕЖІ, КОМПЛЕКСНА МОДЕЛЬ ІНВЕСТУВАННЯ Тема: Розробка комплексної моделі інвестування на ринку криптовалют з використанням часових рядів та нейронних мереж. Об’єкт дослідження: інвестиційна діяльність на ринку криптовалют з використанням аналізу часових рядів та методів нейронних мереж. Предмет дослідження: розробка комплексної моделі інвестування, яка базується на аналізі часових рядів цін криптовалют та використанні нейронних мереж для прогнозування майбутніх цін. Мета роботи: дослідити можливості використання часових рядів та нейронних мереж для розробки ефективної моделі інвестування на ринку криптовалют, здатної прогнозувати майбутні цінові зміни з високою точністю. Методи дослідження: аналіз часових рядів, використання нейронних мереж для прогнозування, статистичний аналіз даних. Актуальність: зростаюча популярність ринку криптовалют та його висока волатильність створюють нові можливості для інвесторів. Розробка ефективної моделі інвестування на основі аналізу часових рядів та нейронних мереж може допомогти інвесторам зробити кращі рішення щодо торгівлі. Результати роботи: був розроблений програмний продукт на мові програмування Python, який використовує аналіз часових рядів та нейронних мереж для прогнозування майбутніх цін. Проведені експерименти та тестування показали, що модель має високу точність в короткостроковому прогнозуванні цін та може бути ефективно використана в інвестиційній діяльності.Diploma thesis: 88 p., 19 figures, 1 appendix, 17 sources. DATA FILTERING, TIME SERIES, NEURAL NETWORKS, COMPLEX INVESTMENT MODEL Theme: Development of a comprehensive model of investment in the cryptocurrency market using time series and neural networks. Object of research: investment activity in the cryptocurrency market using time series analysis and neural network methods. Subject of research: development of a comprehensive investment model based on the analysis of time series of cryptocurrency prices and the use of neural networks to predict future price trends. Purpose: explore the possibilities of using time series and neural networks to develop an effective investment model in the cryptocurrency market capable of predicting future price changes with high accuracy. Research methods: time series analysis, use of neural networks for forecasting, statistical data analysis. Relevance: The growing popularity of the cryptocurrency market and its high volatility create new opportunities for investors. Developing an effective investment model based on time series analysis and neural networks can help investors make better trading decisions. Results: A Python software product was developed that uses time series analysis and neural networks to predict future prices. Experiments and testing have shown that the model is highly accurate in short-term price forecasting and can be effectively used in investment activities

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277

Retail Demand Forecasting

Sales and demand forecasting is one the most critical tasks of enterprises. It lays the foundation for many other essential business assumptions, such as cash flows, profit margins, turnover, capacity planning, and capital expenditure. This report presents a solution for the case study of forecasting monthly sales of one of the largest retail stores in Europe, Rossman chain stores. There are three steps in which this problem will be handled. First, a complete and comprehensive exploratory data analysis will be done to understand the data and perform feature engineering. Secondly, a time series will be modeled by autoregressive models using machine learning and neural networks. Thirdly, these models will be evaluated with standard time series evaluation metrics. Some of the commonly used approaches to achieving the prediction value or models include the ARIMA and classification-based modeling techniques for forecasting. The literature review indicates that these aspects are hard to choose due to the need for matching the supply and demand of consumers being critical as more consumers prefer more reliable companies and companies that can consistently deliver on what they need and when they need. People have underappreciated machine learning’s ability to make data-driven predictions. Still, given the analysis results, companies are now starting to realize its potential and are investing more in this technology

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding