Abstract
To compare the forecast accuracy, Artificial Neural Networks and Autoregressive Integrated Moving Average models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between short and long-term time series of stock closing prices from Palestine.
| Original language | English |
|---|---|
| Pages (from-to) | 14-28 |
| Number of pages | 15 |
| Journal | Electronic Journal of Applied Statistical Analysis |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
Keywords
- ARIMA
- Artificial Neural Network
- Forecasts
- Stock Prices
- Time Series
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
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