Predicting financial markets with Google Trends and not so random keywords

We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade backtesting system, we verify that random finance-related keywords do not to contain more exploitable predictive information than random keywords related to illnesses, classic cars and arcade games. We however show that other keywords applied on suitable assets yield robustly profitable strategies, thereby confirming the intuition of Preis et al. (2013)Comment: 8 pages, 4 figures. First names and last names swappe

Forecasting trends with asset prices

In this paper, we consider a stochastic asset price model where the trend is an unobservable Ornstein Uhlenbeck process. We first review some classical results from Kalman filtering. Expectedly, the choice of the parameters is crucial to put it into practice. For this purpose, we obtain the likelihood in closed form, and provide two on-line computations of this function. Then, we investigate the asymptotic behaviour of statistical estimators. Finally, we quantify the effect of a bad calibration with the continuous time mis-specified Kalman filter. Numerical examples illustrate the difficulty of trend forecasting in financial time series.Comment: 26 pages, 11 figure

Non-additive Lie centralizer of strictly upper triangular matrices

Let ℱ be a field of zero characteristic, let Nn(ℱ) denote the algebra of n X n strictly upper triangular matrices with entries in ℱ, and let ƒ : Nₙ(ℱ) → Nₙ(ℱ) be a non-additive Lie centralizer of Nₙ(ℱ), that is, a map satisfying that ƒ([X; Y ]) = [ƒ(X); Y ] for all X; Y ∈ Nₙ(ℱ). We prove that ƒ(X) = ⋋ X + ƞ (X) where ⋋ ∈ ℱ and ƞ is a map from Nₙ(ℱ) into its center Ƶ (Nₙ(F)) satisfying that ƞ([X; Y ]) = 0 for every X; Y in Nₙ(ℱ).peerReviewe

Design And Implementation Of Photovoltaic Water Pumping System

The solar photovoltaic system is considered as one of the most promising applications for a future sustainable supply power notably in remote areas where the electricity is unavailable or unreliable. Due to the continuous improvement of solar cell technology and the decrease in manufacturing cost, photovoltaic powered water pumps have received considerable attention in the last few years to satisfy the basic need for a large proportion of the world's rural population. This dissertation deals with the modeling and simulation of a photovoltaic water pumping system consisting of PV panel as a generator, power conditioning unit that constitutes of a buck-boost chopper and single phase full bridge inverter to feed a single phase induction motor actuating centrifugal pump. The pump feeds the water tank in order to use it when the sun is not shining. The simulation was carried out through MA TLAB/SIMULINK software using the equivalent electric circuit of each component for each subsystem then the overall system consisting of the subsystems shows the feasibility of the entire photovoltaic water pumping system. This research gives an emphasis on power electronic stage, a DC/DC buck boost chopper was designed and realized, showing its efficiency when connecting to the system. This work stresses on system sizing which is important in order to design a successful installation, HOMER software has been used to recognize the system sizing and it shows a high performance in term of sensitivity analysis of a wide range of input which may affect the system's behavior. The system sizing has been achieved reliably and economically through two criteria which are Annual Capacity Shortage (ACS) and Levelized Cost of Energy (LCE), the results could then serve as a starting point for the designing and optimizing of a successful installation. A prototype hardware of water pumping system which can be used for domestic purposes was built and tested, the experimental results were satisfactory

Software architectures: multi-scale refinement

We propose a multi-scale modeling approach for complex software system architecture description. The multi-scale description may help to obtain meaningful granularities of these systems and to understand and master their complexity. This vision enables an architect designer to express constraints concerning different description levels, oriented to facilitate adaptability management. We define a correct-by-design approach that allows a given abstract architectural description to be refined into architecture models. We follow a progressive refinement process based on model transformations; it begins with a coarse-grain description and ends with a fine-grain description that specifies design details. The adaptability property management is performed through model transformation operations. The model transformation ensures the correctness of UML description, and the correctness of the modeled system. We experimented our approach with a use case that models a smart home system for the monitoring of elderly and disabled persons at home

ISAR Image formation with a combined Empirical Mode Decomposition and Time-Frequency Representation

International audienceIn this paper, a method for Inverse Synthetic Aperture Radar (ISAR) image formation based on the use of the Complex Empirical Mode Decomposition (CEMD) is proposed. The CEMD [1] which based on the Empirical Mode Decomposition (EMD) is used in conjunction with a Time-Frequency Representation (TFR) to estimate a 3-D time-range-Doppler Cubic image, which we can use to effectively extract a sequence of ISAR 2-D range-Doppler images. The potential of the proposed method to construct ISAR image is illustrated by simulations results performed on synthetic data and compared to 2-D Fourier Transform and TFR methods. The simulation results indicate that this method can provide ISAR images with a good resolution. These results demonstrate the potential application of the proposed method for ISAR image formation

Study of Fuzzy Logic-based Controller for Diff-Serv Bandwidth Broking

Combining both voice and data on the same network infrastructure results in need for an advanced network which is to be simple and scalable.  This resulted in new approach for Ethernet where Differentiated Service (Diff-Serv) is introduced. This is taken into consideration as well as QoS as a way of providing class of service to end users. Therefore, optimizing available bandwidth efficacy is one of the goals of this work which is centered on investigating the impact of crucial factors on performance of implementing a fuzzy logic controller. These factors can be external or internal and will be manipulated by Fuzzy Logic controller that will work as bandwidth broker to give each user his optimal Code Point (CP). In this work, the CP will not only consider packet loss rate as external factor  to check the congestion, but it will also consider the internal factors which are a combination of both service-level agreement (SLA) and the type of application being used. This CP will be marked in the transmitted packets, and then the router will check that and will treat it as agreed between user and administrator