import streamlit as st import pandas as pd import matplotlib.pyplot as plt import pytorch_lightning as pl from neuralforecast.core import NeuralForecast from neuralforecast.models import NHITS, TimesNet, LSTM, TFT from neuralforecast.losses.pytorch import HuberMQLoss from neuralforecast.utils import AirPassengersDF import time @st.cache_resource def load_model(path, freq): nf = NeuralForecast.load(path=path) return nf @st.cache_resource def load_all_models(): # Paths for saving models nhits_paths = { 'D': './M4/NHITS/daily', 'M': './M4/NHITS/monthly', 'H': './M4/NHITS/hourly', 'W': './M4/NHITS/weekly', 'Y': './M4/NHITS/yearly' } timesnet_paths = { 'D': './M4/TimesNet/daily', 'M': './M4/TimesNet/monthly', 'H': './M4/TimesNet/hourly', 'W': './M4/TimesNet/weekly', 'Y': './M4/TimesNet/yearly' } lstm_paths = { 'D': './M4/LSTM/daily', 'M': './M4/LSTM/monthly', 'H': './M4/LSTM/hourly', 'W': './M4/LSTM/weekly', 'Y': './M4/LSTM/yearly' } tft_paths = { 'D': './M4/TFT/daily', 'M': './M4/TFT/monthly', 'H': './M4/TFT/hourly', 'W': './M4/TFT/weekly', 'Y': './M4/TFT/yearly' } nhits_models = {freq: load_model(path, freq) for freq, path in nhits_paths.items()} timesnet_models = {freq: load_model(path, freq) for freq, path in timesnet_paths.items()} lstm_models = {freq: load_model(path, freq) for freq, path in lstm_paths.items()} tft_models = {freq: load_model(path, freq) for freq, path in tft_paths.items()} return nhits_models, timesnet_models, lstm_models, tft_models def generate_forecast(model, df): forecast_df = model.predict(df=df) return forecast_df def determine_frequency(df): df['ds'] = pd.to_datetime(df['ds']) df = df.set_index('ds') freq = pd.infer_freq(df.index) return freq def plot_forecasts(forecast_df, train_df, title): fig, ax = plt.subplots(1, 1, figsize=(20, 7)) plot_df = pd.concat([train_df, forecast_df]).set_index('ds') historical_col = 'y' forecast_col = next((col for col in plot_df.columns if 'median' in col), None) lo_col = next((col for col in plot_df.columns if 'lo-90' in col), None) hi_col = next((col for col in plot_df.columns if 'hi-90' in col), None) if forecast_col is None: raise KeyError("No forecast column found in the data.") plot_df[[historical_col, forecast_col]].plot(ax=ax, linewidth=2, label=['Historical', 'Forecast']) if lo_col and hi_col: ax.fill_between( plot_df.index, plot_df[lo_col], plot_df[hi_col], color='blue', alpha=0.3, label='90% Confidence Interval' ) ax.set_title(title, fontsize=22) ax.set_ylabel('Value', fontsize=20) ax.set_xlabel('Timestamp [t]', fontsize=20) ax.legend(prop={'size': 15}) ax.grid() st.pyplot(fig) def select_model_based_on_frequency(freq, nhits_models, timesnet_models, lstm_models, tft_models): if freq == 'D': return nhits_models['D'], timesnet_models['D'], lstm_models['D'], tft_models['D'] elif freq == 'M': return nhits_models['M'], timesnet_models['M'], lstm_models['M'], tft_models['M'] elif freq == 'H': return nhits_models['H'], timesnet_models['H'], lstm_models['H'], tft_models['H'] elif freq in ['W', 'W-SUN']: return nhits_models['W'], timesnet_models['W'], lstm_models['W'], tft_models['W'] elif freq in ['Y', 'Y-DEC']: return nhits_models['Y'], timesnet_models['Y'], lstm_models['Y'], tft_models['Y'] else: raise ValueError(f"Unsupported frequency: {freq}") def select_model(horizon, model_type, max_steps=200): if model_type == 'NHITS': return NHITS(input_size=5 * horizon, h=horizon, max_steps=max_steps, stack_types=3*['identity'], n_blocks=3*[1], mlp_units=[[256, 256] for _ in range(3)], n_pool_kernel_size=3*[1], batch_size=32, scaler_type='standard', n_freq_downsample=[12, 4, 1], loss=HuberMQLoss(level=[90])) elif model_type == 'TimesNet': return TimesNet(h=horizon, input_size=horizon * 5, hidden_size=16, conv_hidden_size=32, loss=HuberMQLoss(level=[90]), scaler_type='standard', learning_rate=1e-3, max_steps=max_steps, val_check_steps=200, valid_batch_size=64, windows_batch_size=128, inference_windows_batch_size=512) elif model_type == 'LSTM': return LSTM(h=horizon, input_size=horizon * 5, loss=HuberMQLoss(level=[90]), scaler_type='standard', encoder_n_layers=2, encoder_hidden_size=64, context_size=10, decoder_hidden_size=64, decoder_layers=2, max_steps=max_steps) elif model_type == 'TFT': return TFT(h=horizon, input_size=horizon, hidden_size=16, loss=HuberMQLoss(level=[90]), learning_rate=0.005, scaler_type='standard', windows_batch_size=128, max_steps=max_steps, val_check_steps=200, valid_batch_size=64, enable_progress_bar=True) else: raise ValueError(f"Unsupported model type: {model_type}") def forecast_time_series(df, model_type, freq, horizon, max_steps=200): start_time = time.time() # Start timing if freq: df['ds'] = pd.date_range(start='1970-01-01', periods=len(df), freq=freq) else: freq = determine_frequency(df) st.write(f"Determined frequency: {freq}") df['ds'] = pd.to_datetime(df['ds'], errors='coerce') df = df.dropna(subset=['ds']) model = select_model(horizon, model_type, max_steps) forecast_results = {} st.write(f"Generating forecast using {model_type} model...") forecast_results[model_type] = generate_forecast(model, df) for model_name, forecast_df in forecast_results.items(): plot_forecasts(forecast_df, df, f'{model_name} Forecast Comparison') end_time = time.time() # End timing time_taken = end_time - start_time st.success(f"Time taken for {model_type} forecast: {time_taken:.2f} seconds") @st.cache_data def load_default(): df = AirPassengersDF.copy() return df def transfer_learning_forecasting(): st.title("Transfer Learning Forecasting") # Upload dataset uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"]) if uploaded_file: df = pd.read_csv(uploaded_file) else: df = load_default() nhits_models, timesnet_models, lstm_models, tft_models = load_all_models() # Model selection and forecasting st.subheader("Model Selection and Forecasting") model_choice = st.selectbox("Select model", ["NHITS", "TimesNet", "LSTM", "TFT"]) horizon = st.number_input("Forecast horizon", value=18) # Determine frequency of data frequency = determine_frequency(df) st.write(f"Detected frequency: {frequency}") # Load pre-trained models nhits_model, timesnet_model, lstm_model, tft_model = select_model_based_on_frequency(frequency, nhits_models, timesnet_models, lstm_models, tft_models) forecast_results = {} start_time = time.time() # Start timing if model_choice == "NHITS": forecast_results['NHITS'] = generate_forecast(nhits_model, df) elif model_choice == "TimesNet": forecast_results['TimesNet'] = generate_forecast(timesnet_model, df) elif model_choice == "LSTM": forecast_results['LSTM'] = generate_forecast(lstm_model, df) elif model_choice == "TFT": forecast_results['TFT'] = generate_forecast(tft_model, df) for model_name, forecast_df in forecast_results.items(): plot_forecasts(forecast_df, df, f'{model_name} Forecast') end_time = time.time() # End timing time_taken = end_time - start_time st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds") def dynamic_forecasting(): st.title("Dynamic Forecasting") # Upload dataset uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"]) if uploaded_file: df = pd.read_csv(uploaded_file) else: df = load_default() # Dynamic forecasting st.subheader("Dynamic Model Selection and Forecasting") dynamic_model_choice = st.selectbox("Select model for dynamic forecasting", ["NHITS", "TimesNet", "LSTM", "TFT"], key="dynamic_model_choice") dynamic_horizon = st.number_input("Forecast horizon", value=18) dynamic_max_steps = st.number_input('Max steps', value=200) # Determine frequency of data frequency = determine_frequency(df) st.write(f"Detected frequency: {frequency}") forecast_time_series(df, dynamic_model_choice, frequency, dynamic_horizon, dynamic_max_steps) # Define the main navigation pg = st.navigation({ "Overview": [ # Load pages from functions st.Page(transfer_learning_forecasting, title="Transfer Learning Forecasting", default=True, icon=":material/query_stats:"), st.Page(dynamic_forecasting, title="Dynamic Forecasting", icon=":material/monitoring:"), ] }) try: pg.run() except Exception as e: st.error(f"Something went wrong: {str(e)}", icon=":material/error:")