#AIImpactOnForex Time series forecasting for currency movements using AI involves applying machine learning and deep learning techniques to predict future exchange rates based on historical data. The process typically includes: 1. Data Collection: Gathering historical exchange rate data and relevant economic indicators (interest rates, inflation, geopolitical events). 2. Preprocessing: Cleaning and normalizing the data to ensure it's suitable for analysis. This may involve handling missing values, outliers, and resampling to a consistent time interval. 3. Feature Engineering: Creating additional features from the raw data, such as moving averages, volatility, and momentum indicators, to provide more insights into market trends. 4. Model Selection: Applying machine learning algorithms like ARIMA (AutoRegressive Integrated Moving Average), LSTM (Long Short-Term Memory networks), and other deep learning methods to capture patterns and trends in the data. 5. Training: Using historical data to train the model and fine-tuning the hyperparameters to achieve the best prediction performance. 6. Evaluation: Assessing the model’s performance using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. 7. Prediction: Making future forecasts of currency movements, which can be used for trading, risk management, and investment strategies. AI can uncover complex non-linear relationships and dependencies in currency markets, providing more accurate and adaptive forecasting models compared to traditional methods. However, due to the volatile and noisy nature of financial markets, predictions remain uncertain and should be used with caution.