Time Series

This paper analyses the determinants of transport costs for intra-Latin American trade over a period of six years (1999-2004). The data refer to yearly disaggregated (SITC 5 digit level) maritime trade flows on 277 trade routes. With this data set, a transport costs equation is estimated using linear regression analysis in a panel data framework. The first contribution to the

Capital Asset Pricing Model (CAPM) builds on the portfolio theory and predicts that all investors hold the market portfolio in equilibrium and as such only systematic risks is priced. Yet empirical evidences on this important relation have been yielding mixed results. While some show that idiosyncratic risk can as well be priced, others contradict this. In investigating whether idiosyncratic risks can be priced in the Nigerian stock market, we employed two-step estimation procedures, namely the time series procedure to determine the beta and idiosyncratic risk for each of the companies and the cross-sectional estimation procedure used on EGARCH model to investigate the impact of these risks on the stock market returns. Our result reveals that systematic risk is priced while the idiosyncratic risk is not priced. Thus, investors in the Nigerian stock market seem to fully diversify away firms specific risk while holding market portfolio. The study also found that volatility clustering ...

This paper is devoted to a discussion of the Discrete Fourier Transform (DFT) representation of a chaotic finite-duration sequence. This representation has the advantage that is itself a finite-duration sequence corresponding to samples equally spaced in the frequency domain. The Fast Fourier Transform (FFT) algoritm allows us an effective computation, and it can be applied to a relatively short time series. DFT representation requirements were analized and applied for determining the order-chaos transition in a nonlinear system described by the equation $x[n+1]=rx[n](1-x[n])$. Its effectiveness was demonstrated by comparing the results with those obtained by calculating the largest Lyapounov exponent for the time series set, obtained from the logistic equation.

Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and periodogram based metrics.

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This...

Thailand experiences severe floods and droughts that affect agriculture New techniques, such as Data-Based- Mechanistic modelling ale being developed to study rainfall and river flow to improve flood and drought alleviation policies and practices. Dynamic Harmonic Regression models are used to analyze rainfall and discharge time series across Thailand to define seasonality, trends and to forecast rainfall and discharge and their spatial distribution. Statistical patterns in the frequency of extreme rainfall and flow periods are identified with a view to improving predictions of medium and Longer-Term rainfall and river flow patterns. The results show temporal and spatial variation within the annual rainfall pattern in the study catchments. For example, the seasonality of the rainfall In the south is less pronounced (more equatorial). The discharge seasonal pattern shows stronger Semi-Annual cycles, with the weakest pattern in the south of country, whereas the strongest discharge sea...

In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the mul- tiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). To better utilize the detailed information in the lower scales of wavelet coef- ficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the indi- vidual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representatio...

Floristic Quality Assessment (FQA) is proposed as a method to assess floristic integrity in Illinois. For the application of FQA, each taxon in the Illinois vascular flora was assigned an integer from 0 to 10 termed a coefficient of conservatism (C). Two basic ecological tenets that the coefficients represent are that plant species differ in their tolerance to disturbance and

1-nearest neighbor (1NN) with Dynamic Time Warping (DTW) distance is a popular time series classification technique. In the last decades, research on DTW aimed to improve its classification accuracy , memory usage, and efficiency. According to a recent study, the appropriate selection of the Warping Window Size (WWS) is crucial for the accuracy of 1NN-DTW. In this work, we consider person identification based on keystroke dynamics as a time series classification task, and we examine whether WWS is crucial in this case. We performed experiments on a real-world dataset containing more than 400 typing sessions obtained from 12 users. In the case of this dataset, each user typed the same fixed text. We found that the aforementioned hypothesis was correct, i.e., the classification accuracy indeed depends on the WWS. Furthermore, according to our observations, in our case, the optimal WWS is less than 3% which is substantially different from 10% that has been used as a default value in various works.