IMARC’s Business and Forecasting Models can serve as critical tools for the evaluation of major transactions and projects.
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macro...
Correction to: Models in digital business and economic forecasting based on big data IoT data visualization technologydoi:10.1007/s00779-021-01649-7BUSINESS forecastingECONOMIC forecastingDATA visualizationBIG dataINTERNET of thingsPUBLISHED articles
This paper considers model selection, estimation and forecasting for a class of integer autoregressive models suitable for use when analysing time series count data. Any number of lags may be entertained, and estimation may be performed by likelihood methods. Model selection is enhanced by the use ...
The results obtained are compared with data and forecasts for Spain and South Korea. The model is used to take into account the mobility of people according to Google COVID-19 Community Mobility Reports. In [18, 19], modified SEIR models are used to obtain scenarios of the epidemic in ...
Power View performs advanced statistical analysis of the data in your line charts to generate forecasts that incorporate trends and seasonal factors. If you want to learn more about these methods and how to customize your forecasts to get the best result
The results show that the forecasting accuracy of the model was higher than traditional hydrological models and other AI models. The study demonstrated the potential of deep-learning methods to overcome the lack of hydrologic data and deficiencies in physical model structure and parameterization, the ...
(id=as.character(id))#call main finnts modeling functionfinn_output<-forecast_time_series(input_data=hist_data,combo_variables=c("id"),target_variable="value",date_type="month",forecast_horizon=3,back_test_scenarios=6,models_to_run=c("arima","ets"),run_global_models=FALSE,run_model_...
We compare a set of estimation methods which can be broadly split into three categories: out-of-sample (OOS), prequential, and cross-validation (CVAL). OOS approaches are commonly used to estimate the performance of models when the data comprises some degree of temporal dependency. The core...
So far, we have restricted our attention to non-seasonal data and non-seasonal ARIMA models. However, ARIMA models are also capable of modelling a wide range of seasonal data.A seasonal ARIMA model is formed by including additional seasonal terms in the ARIMA models we have seen so far. It...