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sha256:968c44ebf5b9d0285ed12fc0698475338119169e2ab3d35f09ef6364780220f6 +size 143 diff --git a/M4/app.py b/M4/app.py new file mode 100644 index 0000000000000000000000000000000000000000..f2fa09a2d45f4e379b0a04365d9a9958d48ebdd8 --- /dev/null +++ b/M4/app.py @@ -0,0 +1,222 @@ + +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 +import time + +# Paths for saving models +nhits_paths = { + 'D': './results/M4/NHITS/daily', + 'M': './results/M4/NHITS/monthly', + 'H': './results/M4/NHITS/hourly', + 'W': './results/M4/NHITS/weekly', + 'Y': './results/M4/NHITS/yearly' +} + +timesnet_paths = { + 'D': './results/M4/TimesNet/daily', + 'M': './results/M4/TimesNet/monthly', + 'H': './results/M4/TimesNet/hourly', + 'W': './results/M4/TimesNet/weekly', + 'Y': './results/M4/TimesNet/yearly' +} + +lstm_paths = { + 'D': './results/M4/LSTM/daily', + 'M': './results/M4/LSTM/monthly', + 'H': './results/M4/LSTM/hourly', + 'W': './results/M4/LSTM/weekly', + 'Y': './results/M4/LSTM/yearly' +} + +tft_paths = { + 'D': './results/M4/TFT/daily', + 'M': './results/M4/TFT/monthly', + 'H': './results/M4/TFT/hourly', + 'W': './results/M4/TFT/weekly', + 'Y': './results/M4/TFT/yearly' +} + +@st.cache_resource +def load_model(path, freq): + nf = NeuralForecast.load(path=path) + return nf + +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()} + +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, freq) + + 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") + +# Streamlit App +st.title("Dynamic and Automatic Time Series 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: + st.warning("Using default data") + df = AirPassengersDF.copy() + +# Model selection and forecasting +st.subheader("Transfer Learning Forecasting") +model_choice = st.selectbox("Select model", ["NHITS", "TimesNet", "LSTM", "TFT"]) +horizon = st.slider("Forecast horizon", 1, 100, 10) + +# 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") + +# Dynamic forecasting +st.subheader("Dynamic Forecasting") +dynamic_model_choice = st.selectbox("Select model for dynamic forecasting", ["NHITS", "TimesNet", "LSTM", "TFT"], key="dynamic_model_choice") +dynamic_horizon = st.slider("Forecast horizon for dynamic forecasting", 1, 100, 10, key="dynamic_horizon") +forecast_time_series(df, dynamic_model_choice, frequency, dynamic_horizon)