import pandas as pd import numpy as np from neuralprophet import NeuralProphet from neuralprophet import set_random_seed import matplotlib.pyplot as plt import streamlit as st from datetime import datetime as dt # import os # os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" st.title('Time Series Forecasting with Neural Prophet') option=st.selectbox('Choose from the following',['Forecasting without events','Forecasting with events']) try: uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"]) @st.cache_data def read_file(uploaded_file): data2=pd.read_csv(uploaded_file) return data2 df=read_file(uploaded_file) ##################################### Option 1 ##################################### if option=='Forecasting without events': daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False) weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True) yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True) n_hist_pred_btn=st.sidebar.number_input('No. of Historical Data Points',0,360,30) epochs_btn=st.sidebar.number_input('Epochs',1,20,5) n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1) loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber']) seasonality_mode_btn=st.sidebar.selectbox('Seasonality Mode',['Additive','Multiplicative']) n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30) with st.expander("Select Date & Observed Value",expanded=True): c1, c2 = st.columns((1, 1)) x=c1.selectbox('Date',df.columns) ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object'] y=c2.selectbox('Observed Value',ycols) with st.expander("Choose the Forecast Period with its Frequency"): c8, c9 = st.columns((1, 1)) periods=int(c8.number_input('Forecast Period',0,365,60)) freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"]) df1=df[[x,y]] df['ds'],df['y']=df[x],df[y] df=df[['ds','y']] df.dropna(inplace=True) df.drop_duplicates(subset=['ds'],inplace=True) df['ds']=pd.to_datetime(df['ds']) df.sort_values(by=['ds'],inplace=True) df=df.reset_index(drop=True) st.header('Dataset') st.dataframe(df1.head()) rmp=st.radio('Run Model',['n','y']) if rmp=='y': set_random_seed(40) m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,seasonality_mode=seasonality_mode_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,) # split into train & test dataset df_train, df_test = m.split_df(df, freq=freq,valid_p=0.2) train_metrics = m.fit(df_train, freq=freq,) test_metrics = m.test(df_test,) import warnings warnings.filterwarnings("ignore") future = m.make_future_dataframe(df=df, n_historic_predictions=n_hist_pred_btn,periods=periods) forecast = m.predict(df=future) final_train_metrics=train_metrics.iloc[len(train_metrics)-1:len(train_metrics)].reset_index(drop=True) final_test_metrics=test_metrics.iloc[len(test_metrics)-1:len(test_metrics)].reset_index(drop=True) fig = m.plot(forecast) fig_comp = m.plot_components(forecast) fig_param = m.plot_parameters() st.header('Train Dataset Metrics') st.dataframe(final_train_metrics) st.header('Test Dataset Metrics') st.dataframe(final_test_metrics) st.header('Forecast Values') st.pyplot(fig) st.header('Trend & Seasonality') st.pyplot(fig_param) st.dataframe(forecast) @st.cache_data def convert_df(df): return df.to_csv(index=False).encode('utf-8') try: forecast_df = convert_df(forecast) if forecast_df is not None: st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',) except: st.warning('Choose Something') ##################################### Option 2 ##################################### if option=='Forecasting with events': daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False) weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True) yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True) n_hist_pred=st.sidebar.number_input('No. of Historical Data Points',0,360,30) epochs_btn=st.sidebar.number_input('Epochs',1,20,5) n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1) loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber']) n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30) with st.expander("Select Date & Observed Value",expanded=True): c1, c2 = st.columns((1, 1)) x=c1.selectbox('Date',df.columns) ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object'] y=c2.selectbox('Observed Value',ycols) with st.expander("Select Event Names & their Dates"): c3, c4 = st.columns((1, 1)) events1=c3.text_input(label='Event 1 Name',value='New Year Eve') eventd1=c3.date_input(label='Event 1 Date Range: ',value=(dt(year=1900, month=1, day=1), dt(year=2030, month=1, day=30)),) events2=c4.text_input(label='Event 2 Name',value='Christmas') eventd2=c4.date_input(label='Event 2 Date Range: ',value=(dt(year=1900, month=1, day=1), dt(year=2030, month=1, day=30)),) with st.expander("Select the Lower & Upper Window for the Events & Seasonality Factor"): c5, c6, c7 = st.columns((1, 1, 1)) lw=c5.number_input('Lower Window',-10,0,-1) uw=c6.number_input('Upper Window',0,10,0) mode=c7.selectbox('Seasonality',['Additive','Multiplicative']) with st.expander("Choose the Forecast Period with its Frequency"): c8, c9 = st.columns((1, 1)) periods=int(c8.number_input('Forecast Period',0,365,60)) freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"]) df1=df[[x,y]] df['ds'],df['y']=df[x],df[y] df=df[['ds','y']] df.dropna(inplace=True) df.drop_duplicates(subset=['ds'],inplace=True) df['ds']=pd.to_datetime(df['ds']) df.sort_values(by=['ds'],inplace=True) df=df.reset_index(drop=True) st.header('Dataset') st.dataframe(df1.head()) rmp=st.radio('Run Model',['n','y']) if rmp=='y': set_random_seed(40) m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,) event1 = pd.DataFrame({'event': events1,'ds': pd.to_datetime(eventd1).date}) event2 = pd.DataFrame({'event': events2,'ds': pd.to_datetime(eventd2).date}) if events2=='': enames=[events1] events_df = pd.concat([event1]) else: enames=[events1,events2] events_df = pd.concat([event1,event2]) events_df=events_df[events_df['event']!=''] for i in range(len(enames)): if enames[i]!='': m=m.add_events([enames[i]],lower_window=lw,upper_window=uw,mode=mode) history_df = m.create_df_with_events(df, events_df) metrics=m.fit(history_df, freq=freq,) import warnings warnings.filterwarnings("ignore") future = m.make_future_dataframe(df=history_df, events_df=events_df,n_historic_predictions=n_hist_pred,periods=periods) forecast = m.predict(df=future) fig = m.plot(forecast) fig_comp = m.plot_components(forecast) fig_param = m.plot_parameters() final_metrics=metrics.iloc[len(metrics)-1:len(metrics)].reset_index(drop=True) st.header('Model Metrics') st.dataframe(final_metrics) st.header('Forecast Values') st.pyplot(fig) st.header('Trend & Seasonality') st.pyplot(fig_param) st.dataframe(forecast) @st.cache_data def convert_df(df): return df.to_csv(index=False).encode('utf-8') try: forecast_df = convert_df(forecast) if forecast_df is not None: st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',) except: st.warning('Choose Something') ##################################################### except: st.warning('Choose Something') st.sidebar.write('### **About**') st.sidebar.info( """ Created by: [Parthasarathy Ramamoorthy](https://www.linkedin.com/in/parthasarathyr97/) (Data Scientist @ Walmart Global Tech) """)