Innovative time series forecasting: auto regressive moving average vs deep networks
نویسندگان
چکیده
منابع مشابه
Using a Fuzzy Auto Regressive Integrated Moving Average Model for Exchange Rate Forecasting
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
متن کاملUsing a Fuzzy Auto Regressive Integrated Moving Average Model for Exchange Rate Forecasting
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
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In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a ...
متن کاملcomparison of auto regressive integrated moving average and artificial neural networks forecasting in mortality of breast cancer
b a c k g r o u n d & aim: one of the common used models in time series is auto regressive integrated moving average (arima) model. arima will do modeling only linearly. artificial neural networks (ann) are modern methods that be used for time series forecasting. these models can identify non-linear relationships among data. the breast cancer has the most mortality of cancers among...
متن کاملHybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield...
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ژورنال
عنوان ژورنال: Entrepreneurship and Sustainability Issues
سال: 2017
ISSN: 2345-0282
DOI: 10.9770/jesi.2017.4.3s(4)