Seasonal Fluctuations and Dynamic Equilibrium Models of Exchange Rate

Most dynamic equilibrium models of exchange rate are not able to generate monthly time series with the typical properties of actual exchange rate. If the exogenous endowments in an equilibrium exchange rate model contain seasonal variations, then the exchange rate will as well. In this paper, we show how in this framework, seasonal preferences can help to remove seasonality of the exchange rate simulated time series.Exchange rate, Equilibrium model, Seasonality.

Improvement of surface water quality variables modelling that incorporates a hydro-meteorological factor: a state-space approach

In this work it is constructed a hydro-meteorological factor to improve the adjustment of statistical time series models, such as state space models, of water quality variables by observing hydrological series (recorded in time and space) in a River basin. The hydro-meteorological factor is incorporated as a covariate in multivariate state space models fitted to homogeneous groups of monitoring sites. Additionally, in the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way

Application of Change-Point Detection to a Structural Component of Water Quality Variables

In this study, methodologies were developed in statistical time series models, such as multivariate state-space models, to be applied to water quality variables in a river basin. In the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way and a change-point detection method is applied to the structural component in order to identify possible changes in the water quality variables in consideration

Time Series Data Mining: A Retail Application Using SAS Enterprise Miner

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. With such data, analytical techniques can be employed to collect information pertaining to historical trends and seasonality. Time series data mining methodology allows users to identify commonalities between sets of time-ordered data. This technique is supported by a variety of algorithms, notably dynamic time warping (DTW). This mathematical technique supports the identification of similarities between numerous time series. The following research aims to provide a practical application of this methodology using SAS Enterprise Miner, an industry-leading software platform for business analytics. Due to the prevalence of time series data in retail settings, a realistic product sales transaction data set was analyzed. This information was provided by dunnhumbyUSA. Interpretations were drawn from output that was generated using “TS nodes” in SAS Enterprise Miner

Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]

Scalable probabilistic modeling and prediction in high dimensional multivariate time-series is a challenging problem, particularly for systems with hidden sources of dependence and/or homogeneity. Examples of such problems include dynamic social networks with co-evolving nodes and edges and dynamic student learning in online courses. Here, we address these problems through the discovery of hierarchical latent groups. We introduce a family of Conditional Latent Tree Models (CLTM), in which tree-structured latent variables incorporate the unknown groups. The latent tree itself is conditioned on observed covariates such as seasonality, historical activity, and node attributes. We propose a statistically efficient framework for learning both the hierarchical tree structure and the parameters of the CLTM. We demonstrate competitive performance in multiple real world datasets from different domains. These include a dataset on students' attempts at answering questions in a psychology MOOC, Twitter users participating in an emergency management discussion and interacting with one another, and windsurfers interacting on a beach in Southern California. In addition, our modeling framework provides valuable and interpretable information about the hidden group structures and their effect on the evolution of the time series

Detecting structural breaks in seasonal time series by regularized optimization

Real-world systems are often complex, dynamic, and nonlinear. Understanding the dynamics of a system from its observed time series is key to the prediction and control of the system's behavior. While most existing techniques tacitly assume some form of stationarity or continuity, abrupt changes, which are often due to external disturbances or sudden changes in the intrinsic dynamics, are common in time series. Structural breaks, which are time points at which the statistical patterns of a time series change, pose considerable challenges to data analysis. Without identification of such break points, the same dynamic rule would be applied to the whole period of observation, whereas false identification of structural breaks may lead to overfitting. In this paper, we cast the problem of decomposing a time series into its trend and seasonal components as an optimization problem. This problem is ill-posed due to the arbitrariness in the number of parameters. To overcome this difficulty, we propose the addition of a penalty function (i.e., a regularization term) that accounts for the number of parameters. Our approach simultaneously identifies seasonality and trend without the need of iterations, and allows the reliable detection of structural breaks. The method is applied to recorded data on fish populations and sea surface temperature, where it detects structural breaks that would have been neglected otherwise. This suggests that our method can lead to a general approach for the monitoring, prediction, and prevention of structural changes in real systems.Comment: Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures (Edited by George Deodatis, Bruce R. Ellingwood and Dan M. Frangopol), CRC Press 2014, Pages 3621-362

Detecting patterns in Time Series Data with applications in Official Statistics

This thesis examines the issue of detecting components or features within time series data in automatic procedures. We begin by introducing the concept of Wavelets and briefly show their usage as a tool for detection. This leads to our first contribution which is a novel method using wavelets for identifying correlation structures in time series data which are often ambiguous with very different contexts. Using the properties of the wavelet transform we show the ability to distinguish between short memory models with changepoints and long memory models. The next two Chapters consider seasonality within data, which is often present in time series used in Offical Statistics. We first describe the historical evolution of identification of seasonality, comparing and contrasting methodology as it has expanded throughout time. Following this, motivated by the increased use of high-frequency time series in Official Statistics and a lack of methods for identifying low-frequency seasonal components within high-frequency data, we present a method for identifying periodicity in a series with the use of a simple wavelet decomposition. Presented with theoretical results and simulations, we show how the seasonality of a series is uniquely represented within a wavelet transform and use this to identify low frequency components which are often overlooked in favour of a trend, with very different interpretations. Finally, beginning with the motivation of forecasting European Area GDP at the current time point, we show the effectiveness of an algorithm which detects the most useful data and structures for a Dynamic Factor Model. We show its effectiveness in reducing forecasting errors but show that under large scale simulation that the recovery of the true structure over two dimensions is a difficult task. All the chapters of this thesis are motivated by, and give applications to, time series from different areas of Official Statistics

Intraday CAC40, DAX and WIG20 returns when the American macro news is announced

We examine the reaction of the returns of CAC40, DAX and WIG20 to the periodically scheduled prominent American macroeconomic data announcements. We investigate returns and volatility dynamics at the time of news arrival as well as interdependence between series within the time of the announcements. The results suggest that the macro announcements from the U.S. market not only explain seasonality observed in these equity markets but also have a significant impact on both returns and volatility. However, the reactions to announcements are different with respect to the type of announcement. Application of dynamic conditional correlation models allows us to decompose the total impact of announcements into the reaction on the domestic market and conditional correlation between the markets.macroeconomic announcements, high-frequency data, volatility

Demand forecasting for fast-moving products in grocery retail

Demand forecasting is a critically important task in grocery retail. Accurate forecasts allow the retail companies to reduce their product spoilage, as well as maximize their profits. Fast-moving products, or products with a lot of sales and fast turnover, are particularly important to forecast accurately due to their high sales volumes. We investigate dynamic harmonic regression, Poisson GLM with elastic net, MLP and two-layer LSTM in fast-moving product demand forecasting against the naive seasonal forecasting baseline. We evaluate two modes of seasonality modelling in neural networks: Fourier series against seasonal decomposition. We specify the full procedure for comparing forecasting models in a collection of product-location sales time series, involving two-stage cross-validation, and careful hyperparameter selection. We use Halton sequences for neural network hyperparameter selection. We evaluate the model results in demand forecasting using hypothesis testing, bootstrapping, and rank comparison methods. The experimental results suggest that the dynamic harmonic regression produces superior results in comparison to Poisson GLM, MLP and two-layer LSTM models for demand forecasting in fast-moving products with long sales histories. We additionally show that deseasonalization results in better forecasts in comparison to Fourier seasonality modelling in neural networks

Modeling the number of people in Venice during COVID-19: a State Space approach

This thesis investigates the effectiveness of protective measures during COVID-19. Specifically, this work is about the use of Dynamic Linear models (DLMs) to analyze the number of people present in photos taken at Campo San Felice in Venice. To obtain this time series, Yolov7, a state of the art model in object detection, was trained on a custom dataset and used to extract the number and the position of people from each photo. Additionally, with this data, the DBSCAN algorithm was applied to identify and analyze the clusters of people present in the different pictures. The DLMs were then fitted to the data and compared to Prophet, a modular regression model developed by Meta. The study found that the DLM was effective in capturing the trends and seasonality of the time series, and highlighted the potential of using DLMs in time series analysis, particularly for analyzing real and complex data.This thesis investigates the effectiveness of protective measures during COVID-19. Specifically, this work is about the use of Dynamic Linear models (DLMs) to analyze the number of people present in photos taken at Campo San Felice in Venice. To obtain this time series, Yolov7, a state of the art model in object detection, was trained on a custom dataset and used to extract the number and the position of people from each photo. Additionally, with this data, the DBSCAN algorithm was applied to identify and analyze the clusters of people present in the different pictures. The DLMs were then fitted to the data and compared to Prophet, a modular regression model developed by Meta. The study found that the DLM was effective in capturing the trends and seasonality of the time series, and highlighted the potential of using DLMs in time series analysis, particularly for analyzing real and complex data