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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)
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