app.py

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

# 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)

    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()  

    # 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)
    
    # Determine frequency of data
    frequency = determine_frequency(df)
    st.write(f"Detected frequency: {frequency}")
    
    forecast_time_series(df, dynamic_model_choice, frequency, dynamic_horizon)
    
# 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:")