A General Framework for Time Series Forecasting Model Using Autoregressive Integrated Moving Average-ARIMA and Transfer Functions

Major current econometric stochastic series forecast research are established on the failure of the scholastic process tests to differentiate between finite and stationary alternative samples of the unit root hypothesis results. The importance of forecast evaluation allows researchers to reasonably monitor and improve forecast performance. While a structured improved forecast framework have often been suggested as one possible alternative, an extended the multivariate model which incorporate distributed-lag period for independent variable gives a unique advantage over the traditional distributed-lag model and the mathematical formulation does essentially guarantee that predicated equation irrespective of the values of the predictor variables. Hence, the primary objective is mainly to determine the likelihood of autoregressive integrated moving average (ARIMA) method for practicable process choice used for predicting key economic variables for a set of market data. Once the process has been known, parameters have been obtained, and the adequacy of the model has been determined, forecasts can be checked for reliability.

A General Framework for Time Series Forecasting Model Using Autoregressive Integrated Moving Average-ARIMA and Transfer Functions

Major current econometric stochastic series forecast research are established on the failure of the scholastic process tests to differentiate between finite and stationary alternative samples of the unit root hypothesis results. The importance of forecast evaluation allows researchers to reasonably monitor and improve forecast performance. While a structured improved forecast framework have often been suggested as one possible alternative, an extended the multivariate model which incorporate distributed-lag period for independent variable gives a unique advantage over the traditional distributed-lag model and the mathematical formulation does essentially guarantee that predicated equation irrespective of the values of the predictor variables. Hence, the primary objective is mainly to determine the likelihood of autoregressive integrated moving average (ARIMA) method for practicable process choice used for predicting key economic variables for a set of market data. Once the process has been known, parameters have been obtained, and the adequacy of the model has been determined, forecasts can be checked for reliability.

Spatial and semantic regularities produce interactive effects in early stages of visual orientation

ABSTRACTLearning environmental regularities allows predicting multiple dimensions of future events such as their location and semantic features. However, few studies have examined how multi-dimensional predictions are implemented, and mechanistic accounts are absent. Using eye tracking study, we evaluated whether predictions of object-location and object-category interact during the earliest stages of orientation. We presented stochastic series so that across four conditions, participants could predict either the location of the next image, its semantic category, both dimensions, or neither. Participants observed images in absence of any task. We modeled saccade latencies using ELATER, a rise-to-threshold model that accounts for accumulation rate (AR), variance of AR over trials, and decision threshold. The main findings were: 1) accumulation-rate scaled with the degree of surprise associated with location of target-presentation (confirmatory result); 2) predictability of semantic-category hindered latencies, but only when images were presented at a surprising location, suggesting a bottleneck in implementing joint predictions; 3) saccades to images that satisfied semantic expectations were associated with larger variance of accumulation-rate than saccades to semantically-surprising images, consistent with a richer repertoire of early evaluative processes for semantically-expected images. Joint impacts of location and target-identity regularity were also identified in analyses of anticipatory fixation offsets. The results indicate a strong interaction between the processing of regularities in object location and identity during stimulus-guided saccades, and suggest these regularities also impact anticipatory, non-stimulus-guided processes.