Predictive modeling
INSTRUCTIONS:
The models are applicable for time-series, often used in finance.
You can test the models by using data from the database. To do that, first,
select an asset form 'use database asset' then 'predictive model' and click on the
predict button. Alternatively, you can supply the model with your own data by uploading
a CSV file. For best result the file should be formatted as follows:
1) First column (optional) date, in order for the backend to properly read it, the
format must be YYYY-MM-DD and dates should
start from the most recent date to the oldest observation (i.e. the most current is at
the top).
2) Second column ( or first if you skip date) data, again structured
from most recent to older data points.
*** The model expects labels, even if there are non,
simply add date/data in the first row!
Model information:
ARIMA is an acronym structured from the combined elements of the prediction
AutoRegression Integration Moving Averages. ARIMA models combine both Autoregression (AR)
and Moving Averages (MA) as weighted components, combining the models.
Also, it works under the strong assumption for stationarity of the input data thus the
Integration component.