Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expe...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

The paper discusses the properties of Auto-Regressive Integrated Moving Average (ARIMA) models and proceeds to estimate a model for monthly evolution annual inflation rate in Moldova from January 2013 October 2021. aim is develop relying exclusively upon historical as an additional instrument forecasting purposes. estimated explains close 97 % variation over model’s estimation period used gener...

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends machine learning (ML) technologies, automated recognition is becoming popular attracting many researchers. In this paper,...

Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we wil...

energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. however, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. accordingly, in this paper a new strategy is proposed for electricity price forecast. the forecast strategy includes wavelet transform (wt...

The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and catch commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Lesser Sardines for next 5 years by different statistical machine learning approaches under climate change scenario. Forecasting is done with without inclusion climatic environmental parameters in models. Exogenous variable...

One of the most natural and primary ways of data collection in wireless sensor networks is to periodically report sensed data values from sensor node to aggregator. However, this kind of data collection mechanism comes at the cost of power consumption and packet collision. In this paper, we developed an automatic ARIMA (Auto Regressive Integrated Moving Average) modeling based data aggregation ...

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis ...

Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural ne...