Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series

In this work, we attempted to find a non-linear dependency in the time series of strawberry production in Huelva (Spain) using a procedure based on metric tests measuring chaos. This study aims to develop a novel method for yield prediction. To do this, we study the system’s sensitivity to initial conditions (exponential growth of the errors) using the maximal Lyapunov exponent. To check the soundness of its computation on non-stationary and not excessively long time series, we employed the method of over-embedding, apart from repeating the computation with parts of the transformed time series. We determine the existence of deterministic chaos, and we conclude that non-linear techniques from chaos theory are better suited to describe the data than linear techniques such as the ARIMA (autoregressive integrated moving average) or SARIMA (seasonal autoregressive moving average) models. We proceed to predict short-term strawberry production using Lorenz’s Analog MethodThis research was funded by Junta de Andalucía. Consejería de la Presidencia, Administración Pública e Interior. Secretaría General de Acción Exterior grant number G/82A/44103/00 0

Change point analysis in a state space framework to monthly temperature data in European cities

In this work, we present time series of monthly average temperatures in several Euro- pean locations which were statistically analyzed using a state space approach, where it is considered a model with a deterministic seasonal component and a stochastic trend. The analysis of smoother prediction of the stochastic trend and its comparison in a tem- poral viewpoint can reveal patterns about warming in Europe. The temperature rise rates in Europe seem to have increased in the last decades when compared with longer periods, hence a change point detection method is applied to the trend component in order to identify these possible changes in the monthly temperature rise rates. The adopted methodology pointed out, for most series a change point in the late eighties.publishe

Teknik Jaringan Syaraf Tiruan Feedforward Untuk Prediksi Harga Saham Pada Pasar Modal Indonesia

To predict the condition of stock price, several technical analysis models have been used and expanded such as MACD, Fourier Transform, Accumulator Swing Index , Stochastic Oscillator etc. For input they are using the various prices such as Open, high, low , close , volume, BID, ASK price, and the output is a graphic that shows the decision whether to sell, buy or hold. Another method to determine the stock price by using Fundamental Analysis method. Fundamental method is an analysis that is based on the ratio or financial report from the existing company. Neural Network System Technology has been implemented in various applications especially in introduce the pattern. This power has attracted several people to use Neural Network for medical, Finance, Investment and marketing. Assuming that the prediction of the output system (next output prediction) is deterministic, than the suitable N.N model to predict it is Feed Forward. The prediction of the stock price is the complex interaction between unstable market and unknown random processes factor. The data from stock price can be determined by time series. If we have daily data from a certain period, for example : Xt(t = 1,2,...) than the stock price for the next period (t+h) can be predicted (the timing used can be in hourly, daily, weekly, monthly or yearly). To get the good prediction, the inputs from several aspects of the share prices have to be input in Neural Network after that the weighing principal can be adapted to minimize the wrong prediction in the first future steps. By using the final weighing, an action is done to done to minimize the total error in the second future steps. Due to that, the risk of Investor's decision to sell or buy the stock can be minimized. This paper will discuss on how to use and implement Time Series Neural Network to predict the stock market in Semen Gresik (SMGR) and Gudang Garam (GGRM