The crucial steps of preprocessing time series data for a forecasting project are covered. Preprocessing ensures the data is clean, well-structured, and ready for model training. Topics include feature scaling, train-test split, and sequence creation, all essential for effective data preparation.

• Feature scaling: feature scaling is important to ensure that all input features are on a similar scale. This helps models like LSTM and ARIMA converge faster and improve their performance. Common techniques for feature scaling include min-max scaling and standardization (z-score normalization). Scaling helps the model focus on the relationships within the data rather than being biased by features with larger ranges;

  scaler = MinMaxScaler(feature_range=(0, 1))
  scaled_train_data = scaler.fit_transform(train_data_raw)
  scaled_test_data = scaler.transform(test_data_raw)
  

• Train-test split: splitting the dataset into training and testing subsets is essential for evaluating model performance. Typically, a time series dataset is split chronologically, with the earlier part of the data used for training and the later part for testing. This ensures that the model is evaluated on data it has not seen before and mimics real-world forecasting scenarios. A common ratio is 80% for training and 20% for testing, but this may vary based on the size and characteristics of the data;

  train_split_ratio = 0.8
  train_size = int(len(price_data) * train_split_ratio)
  train_data_raw = price_data[:train_size]
  test_data_raw = price_data[train_size:]
  

• Sequence creation: in time series forecasting, especially when using models like LSTM, the data needs to be transformed into a sequence format. The sequence creation step involves shaping the data into input-output pairs where each input corresponds to a sequence of past observations, and the output is the predicted value for the next time step. This is crucial for models to learn from previous time steps and make accurate predictions for future steps.

  def create_sequences(data, seq_length):
      xs = []
      ys = []
      for i in range(len(data) - seq_length):
          x = data[i:(i + seq_length)]
          y = data[i + seq_length]
          xs.append(x)
          ys.append(y)
      # Ensure numpy arrays are returned, helps with tensor conversion later
      return np.array(xs), np.array(ys)
  

In summary, preprocessing is a vital step in time series forecasting. By scaling the features, splitting the data for training and testing, and creating sequences for model input, we ensure that the data is well-prepared for accurate and efficient forecasting.