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import streamlit as st
import pandas as pd
from src.data.utils import *
from src.visualization.visualize import *
from src.features.build_features import *
def main():
st.title("Time Series Decomposition Demo")
st.header("Data")
sample_data_selected = st.selectbox(
'Select sample data:', data_set_options)
data, graph_data = import_sample_data(
sample_data_selected, data_set_options)
show_inputted_dataframe(data)
with st.expander("Box Plot:"):
time_series_box_plot(graph_data)
with st.expander("Dist Plot (histogram and violin plot):"):
time_series_violin_and_box_plot(data)
st.header("Time series decomposition")
[decomposition, selected_model_type] = decompose_time_series(data)
if selected_model_type == model_types[0]:
st.subheader('Additive Model')
st.latex(r'''
Y[t] = T[t]+S[t]+e[t]
''')
if selected_model_type == model_types[1]:
st.subheader('Multiplicative Model')
st.latex(r'''
Y[t] = T[t] \times S[t] \times e[t]
''')
standard_decomposition_plot(decomposition)
[trend, seasonal, residual] = extract_trend_seasonal_resid(decomposition)
with st.expander("Time series Line Plot (Y[t])"):
time_series_line_plot(data)
st.latex(r'''=''')
with st.expander("Trend Plot (T[t])"):
st.write('The trend component of the data series.')
st.write('Trend: secular variation(long-term, non-periodic variation)')
time_series_line_plot(trend)
if selected_model_type == model_types[0]:
st.latex(r'''+''')
if selected_model_type == model_types[1]:
st.latex(r'''\times''')
with st.expander("Seasonality Plot (S[t])"):
st.write('The seasonal component of the data series.')
st.write(
'Seasonality: Periodic fluctuations (often at short-term intervals less than a year).')
time_series_line_plot(seasonal)
if selected_model_type == model_types[0]:
st.latex(r'''+''')
if selected_model_type == model_types[1]:
st.latex(r'''\times''')
with st.expander("Residual Plot (e[t])"):
st.write('The residual component of the data series.')
st.write('Residual: What remains after the other components have been removed (describes random, irregular influences).')
st.write(f'Residual mean: {residual.mean():.4f}')
time_series_scatter_plot(residual)
if __name__ == "__main__":
main()
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