ARIMA Model for Accurate Time Series Stocks Forecasting

Short-Term Forecasting with ARIMA Time Series Models

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In par...

Application of a Hybrid ARIMA and Neural Network Model to Water Quality Time Series Forecasting

In this paper the water quality forecasting at the Nanjinguan water quality monitoring station of Yangtze River, China, is presented. The time series data used are weekly water quality data obtained directly from Nanjinguan station measurements over the course of five years. In order to forecast water quality, hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models a...

Time series forecasting using a hybrid ARIMA and neural network model

Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with arti/cial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiorit...

Combining neural network model with seasonal time series ARIMA model

This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SA...