Predicting future values of a data sequence based on historical patterns. This involves modeling trends, seasonality, and noise in data indexed by time. In forex, the currency exchange rates form a time series. Forecasting methods (ranging from ARIMA to ML models like LSTMs) use past price and indicator data to predict future movements. For example, a forecasting model might use the last 30 minutes of tick data to predict the next minute’s price. Effective time-series forecasting allows a forex robot to anticipate price changes and place trades accordingly.

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