Forecasting Bitcoin Prices Using N-BEATS Deep Learning Architecture

The use of computationally intensive systems that employ machine learning algorithms is increasingly common in the field of finance. New state of the art deep learning architectures for time series forecasting are being developed each year making them more accurate than ever. This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA. Prediction errors are measured with Mean Average Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results suggest that the developed N-BEATS model has promising predictive power compared to LSTM and ARIMA models

Forecasting VARMA processes using VAR models and subspace-based state space models

VAR modelling is a frequent technique in econometrics for linear processes. VAR modelling offers some desirable features such as relatively simple procedures for model specification (order selection) and the possibility of obtaining quick non-iterative maximum likelihood estimates of the system parameters. However, if the process under study follows a finite-order VARMA structure, it cannot be equivalently represented by any finite-order VAR model. On the other hand, a finite-order state space model can represent a finite-order VARMA process exactly, and, for state-space modelling, subspace algorithms allow for quick and non-iterative estimates of the system parameters, as well as for simple specification procedures. Given the previous facts, we check in this paper whether subspace-based state space models provide better forecasts than VAR models when working with VARMA data generating processes. In a simulation study we generate samples from different VARMA data generating processes, obtain VAR-based and state-space-based models for each generating process and compare the predictive power of the obtained models. Different specification and estimation algorithms are considered; in particular, within the subspace family, the CCA (Canonical Correlation Analysis) algorithm is the selected option to obtain state-space models. Our results indicate that when the MA parameter of an ARMA process is close to 1, the CCA state space models are likely to provide better forecasts than the AR models. We also conduct a practical comparison (for two cointegrated economic time series) of the predictive power of Johansen restricted-VAR (VEC) models with the predictive power of state space models obtained by the CCA subspace algorithm, including a density forecasting analysis.subspace algorithms; VAR; forecasting; cointegration; Johansen; CCA

Evaluating the Performance of Infectious Disease Forecasts: A Comparison of Climate-Driven and Seasonal Dengue Forecasts for Mexico

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model

Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models

While the simulation of stochastic time series is challenging due to their inherently complex nature, this is compounded by the arbitrary and widely accepted feature data usage methods frequently applied during the model development phase. A pertinent context where these practices are reflected is in the forecasting of drought events. This chapter considers optimization of feature data usage by sampling daily data sets via self-organizing maps to select representative training and testing subsets and accordingly, improve the performance of effective drought index (EDI) prediction models. The effect would be observed through a comparison of artificial neural network (ANN) and an autoregressive integrated moving average (ARIMA) models incorporating the SOM approach through an inspection of commonly used performance indices for the city of Brisbane. This study shows that SOM-ANN ensemble models demonstrate competitive predictive performance for EDI values to those produced by ARIMA models

Forecasting the Impact of Product-Harm Events on Firm Value by Leveraging Negative Word of Mouth

Product-harm events are always a nightmare for all stakeholders. Analysts believe that defective items may not only provide risks to the general population, but can likewise cause critical monetary and reputational harm to the firms. Since ignoring a problem does not lead to having it go away, more research is needed to shed new light on the way crisis and risk communication should take place once necessary. Prior study has suggested the complexities of consumer word of mouth effects and how to accurately forecast the impacts of product-harm events on firm value as important subjects. This study extracts the sentiments of consumer complaints in the context of product defects and examines if including consumer sentiment in time series models can improve forecasting performance. Authors make an empirical comparison between two multivariate time series forecasting methods: VAR (vector autoregressive model), and deep learning LSTM (long short-term memory model). Unique datasets, containing five-year data of all automobile nameplates for three major manufacturers in the U.S. are analyzed. The one-step rolling forecast approach is applied to validate time series forecasting values. The results of mean RMSE suggest that LSTM outperforms VAR predictive ability of firm value, and on average obtains 59.02% reduction in error rates when compared with error rates of VAR. It is also noticed that adding consumer sentiment in modeling can improve the predictive performance of both LSTM and VAR models; however, VAR-based models make greater progress in predictive error reduction with consumer sentiment. Implications for marketing research and managerial contributions are discussed

Essays on Panel Data Prediction Models

Forward-looking analysis is valuable for policymakers as they need effective strategies to mitigate imminent risks and potential challenges. Panel data sets contain time series information over a number of cross-sectional units and are known to have superior predictive abilities in comparison to time series only models. This PhD thesis develops novel panel data methods to contribute to the advancement of short-term forecasting and nowcasting of macroeconomic and environmental variables. The two most important highlights of this thesis are the use of cross-sectional dependence in panel data forecasting and to allow for timely predictions and ‘nowcasts’.Although panel data models have been found to provide better predictions in many empirical scenarios, forecasting applications so far have not included cross-sectional dependence. On the other hand, cross-sectional dependence is well-recognised in large panels and has been explicitly modelled in previous causal studies. A substantial portion of this thesis is devoted to developing cross-sectional dependence in panel models suited to diverse empirical scenarios. The second important aspect of this work is to integrate the asynchronous release schedules of data within and across panel units into the panel models. Most of the thesis emphasises the pseudo-real-time predictions with efforts to estimate the model on the data that has been released at the time of predictions, thus trying to replicate the realistic circumstances of delayed data releases.Linear, quantile and non-linear panel models are developed to predict a range of targets both in terms of their meaning and method of measurement. Linear models include panel mixed-frequency vector-autoregression and bridge equation set-ups which predict GDP growth, inflation and CO2 emissions. Panel quantile regressions and latent variable discrete choice models predict growth-at-risk and extreme episodes of cross-border capital flows, respectively. The datasets include both international cross-country panels as well as regional subnational panels. Depending on the nature of the model and the prediction targets, different precision criteria evaluate the accuracy of the models in out-of-sample settings. The generated predictions beat respective standard benchmarks in a more timely fashion

Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy

Multiple time series models with stochastic regressors are considered and primary attention is given to vector autoregressions (VAR’s) with trending mechanisms that may be stochastic, deterministic or both. In a Bayesian framework, the data density in such a system implies the existence of a time series “Bayes model” and “Bayes measure” of the data. These are predictive models and measures for the next period observation given the historical trajectory to the present. Issues of model selection, hypothesis testing and forecast evaluation are all studied within the context of these models and the measures are used to develop selection criteria, test statistics and encompassing tests within the compass of the same statistical methodology. Of particular interest in applications are lag order and trend degree, causal effects, the presence and number of unit roots in the system, and for integrated series the presence of cointegration and the rank of the cointegration space, which can be interpreted as an order selection problem. In data where there is evidence of mildly explosive behavior we also wish to allow for the presence of co-motion among variables even though they are individually not modelled as integrated series. The paper develops a statistical framework for addressing these features of trending multiple time series and reports an extended empirical application of the methodology to a model of the US economy that sets out to explain the behavior of and to forecast interest rates, unemployment, money stock, prices and income. The performance of a data-based, evolving “Bayes model” of these series is evaluated against some rival fixed format VAR’s, VAR’s with Minnesota priors (BVARM’s) and univariate models. The empirical results show that fixed format VAR’s and BVARM’s all perform poorly in forecasting exercises in comparison with evolving “Bayes models” that explicitly adapt in form as new data becomes available

A Comparative Study of Path Performance Metrics Predictors

peer reviewedUsing quality-of-service (QoS) metrics for Internet traffic is expected to improve greatly the performance of many network enabled applications, such as Voice-over-IP (VoIP) and video conferencing. However, it is not possible to constantly measure path performance metrics (PPMs) such as delay and throughput without interfering with the network. In this work, we focus on PPMs measurement scalability by considering machine learning techniques to estimate predictive models from past PPMs observations. Using real data collected from PlanetLab, we provide a comparison between three different predictors: AR(MA) models, Kalman filters and support vector machines (SVMs). Some predic- tors use delay and throughput jointly to take advantage of the possible relationship between PPMs, while other predictors consider PPMs individually. Our current results illustrate that the best performing model is an individual SVM specific to each time series. Overall, delay can be predicted with very good accuracy while accurate forecasting of throughput remains an open problem

Hydrological Drought Forecasting Using a Deep Transformer Model

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE = 0.11, MAE = 0.21, RSME = 0.31, and R2 = 0.92 for the Chattahoochee station, and MSE = 0.06, MAE = 0.19, RSME = 0.23, and R2 = 0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere