deep-learning-forecasting

name deep-learning-forecasting description Forecasts time series using recurrent neural networks (RNN, LSTM, GRU) with ForecasterRnn and the create_and_compile_model helper. Covers model architecture, training, and multi-series deep learning. Use when the user wants to use deep learning / neural networks for time series forecasting. Deep Learning Forecasting (RNN/LSTM) When to Use Use ForecasterRnn when: You have large datasets (thousands of observations) Complex nonlinear patterns that tree-based models struggle with Multi-series problems where series share deep temporal patterns Requirements : pip install skforecast[deeplearning] (installs keras) Related skills Prerequisite : choosing-a-forecaster (confirm ForecasterRnn is the right choice for the data size and pattern) Prerequisite : feature-engineering (RNN models still benefit from cyclical / calendar exogenous features) Next : hyperparameter-optimization (tune RNN architecture and training hyperparameters) Next : prediction-intervals (only conformal intervals are supported for ForecasterRnn ) Stop Conditions Scan before writing code. Each row lists a rule, the symptom when it is broken, and the recovery. Full pitfall catalog: the troubleshooting-common-errors skill. Rule Symptom Recovery lags in ForecasterRnn must match create_and_compile_model(..., lags=...) Input shape mismatch error during fit Use the same lags value in both calls ForecasterRnn supports only method='conformal' for intervals Error when calling predict_interval(method='bootstrapping') Use method='conformal' Pass exog to create_and_compile_model if exog is used in fit() / predict() Architecture mismatch or failure when predicting with exog Build the model with the same exog you train on Scale inputs with transformer_series=MinMaxScaler() Poor convergence; RNNs are scale-sensitive Always set a scaler on transformer_series Quick Start import pandas as pd from skforecast.deep_learning import ForecasterRnn, create_and_compile_model from sklearn.preprocessing import MinMaxScaler # 1. Prepare data (DataFrame with DatetimeIndex, columns = series) series = pd.read_csv( 'data.csv' , index_col= 'date' , parse_dates= True ) series = series.asfreq( 'h' ) # 2. Create and compile a Keras model model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels=series.columns.tolist(), # All series recurrent_layer= 'LSTM' , # 'LSTM', 'GRU', or 'RNN' recurrent_units=[ 64 , 32 ], # Units per recurrent layer dense_units=[ 32 ], # Units per dense layer compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, ) # 3. Create forecaster forecaster = ForecasterRnn( levels=series.columns.tolist(), lags= 48 , estimator=model, transformer_series=MinMaxScaler(feature_range=( 0 , 1 )), fit_kwargs={ 'epochs' : 50 , 'batch_size' : 32 , 'verbose' : 0 }, ) # 4. Train forecaster.fit(series=series) # 5. Predict predictions = forecaster.predict(steps= 24 ) Model Architecture with create_and_compile_model from skforecast.deep_learning import create_and_compile_model # Simple LSTM model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels= 'target' , recurrent_layer= 'LSTM' , recurrent_units=[ 64 ], dense_units=[ 32 ], compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, ) # Stacked LSTM (multiple recurrent layers) model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels=series.columns.tolist(), recurrent_layer= 'LSTM' , recurrent_units=[ 128 , 64 , 32 ], # 3 stacked LSTM layers dense_units=[ 64 , 32 ], # 2 dense layers compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, ) # GRU variant (faster training) model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels= 'target' , recurrent_layer= 'GRU' , recurrent_units=[ 64 ], dense_units=[ 32 ], compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, ) # Advanced: customize layer kwargs model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels=series.columns.tolist(), recurrent_layer= 'LSTM' , recurrent_units=[ 128 , 64 ], recurrent_layers_kwargs={ 'activation' : 'tanh' }, # default dense_units=[ 64 ], dense_layers_kwargs={ 'activation' : 'relu' }, # default output_dense_layer_kwargs={ 'activation' : 'linear' }, # default compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, model_name= 'my_lstm_model' , ) With Exogenous Variables When using exogenous variables, pass exog to create_and_compile_model so it builds the correct architecture (uses TimeDistributed layers internally). # exog must be a DataFrame covering the training period exog = pd.DataFrame({ 'temperature' : [...], 'holiday' : [...]}, index=series.index) model = create_and_compile_model( series=series, lags= 48 , steps= 24 , levels=series.columns.tolist(), exog=exog, # Passes exog info to build architecture recurrent_layer= 'LSTM' , recurrent_units=[ 64 , 32 ], dense_units=[ 32 ], compile_kwargs={ 'optimizer' : 'adam' , 'loss' : 'mse' }, ) forecaster = ForecasterRnn( levels=series.columns.tolist(), lags= 48 , estimator=model, fit_kwargs={ 'epochs' : 50 , 'batch_size' : 32 , 'verbose' : 0 }, ) forecaster.fit(series=series, exog=exog) predictions = forecaster.predict(steps= 24 , exog=exog_test) # exog_test covers forecast horizon Custom Keras Model import keras # Build your own model for full control (single level, no exog) # Output units = steps * n_levels. For 1 level: steps. For N levels: steps * N + Reshape. inputs = keras.layers.Input(shape=( 48 , 1 )) # (lags, n_features) x = keras.layers.LSTM( 64 , return_sequences= True )(inputs) x = keras.layers.LSTM( 32 )(x) x = keras.layers.Dense( 32 , activation= 'relu' )(x) outputs = keras.layers.Dense( 24 )(x) # steps * n_levels (here 24 * 1) model = keras.Model(inputs=inputs, outputs=outputs) model. compile (optimizer= 'adam' , loss= 'mse' ) forecaster = ForecasterRnn( levels= 'target' , lags= 48 , estimator=model, transformer_series=MinMaxScaler(feature_range=( 0 , 1 )), fit_kwargs={ 'epochs' : 100 , 'batch_size' : 32 }, ) Multi-series custom model : For N levels, the output layer should be Dense(steps * n_levels) followed by Reshape((steps, n_levels)) . Prediction Intervals # ForecasterRnn supports conformal prediction only forecaster.fit(series=series, store_in_sample_residuals= True ) predictions = forecaster.predict_interval( steps= 24 , method= 'conformal' , # Only 'conformal' supported interval=[ 0.1 , 0.9 ], use_in_sample_residuals= True , use_binned_residuals= True , # Better calibration with binned residuals ) Backtesting from skforecast.model_selection import backtesting_forecaster_multiseries, TimeSeriesFold cv = TimeSeriesFold( steps= 24 , initial_train_size= len (series) - 200 , refit= False , # Retraining RNNs is expensive; set True only if needed ) metric, predictions = backtesting_forecaster_multiseries( forecaster=forecaster, series=series, cv=cv, metric= 'mean_absolute_error' , ) Common Mistakes Not scaling data : RNNs are sensitive to scale. Always use transformer_series=MinMaxScaler() . Too few epochs : Deep learning needs more training iterations. Start with 50-100 epochs. Wrong input shape : The lags parameter in ForecasterRnn and create_and_compile_model must match. Refit=True in backtesting : Retraining RNNs at every fold is very slow — use refit=False or refit=5 . No GPU : Training is slow on CPU. Use GPU if available. Using predict_interval(method='bootstrapping') : ForecasterRnn only supports method='conformal' . Forgetting exog in create_and_compile_model : If you use exog in fit() / predict() , you must also pass exog when building the model so the architecture accounts for the extra input features. References See references/architecture-options.md for the complete create_and_compile_model signature, recurrent layer types, output shape rules, exog architecture, custom Keras model requirements, and fit_kwargs options.