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#!/usr/bin/env python
# coding: utf-8
# In[3]:
import pandas as pd
import pmdarima as pm
import matplotlib.pyplot as plt
import gradio as gr
import io
import warnings
warnings.simplefilter("ignore")
def predict_timeseries(data_file):
# Load CSV file into a pandas DataFrame
data = pd.read_csv(data_file.name, index_col=[0], parse_dates=True)
# Convert date column to datetime object
data.index = pd.to_datetime(data.index)
# Fit the auto ARIMA model
model = pm.auto_arima(data.values, seasonal=True, m=12)
# Get ARIMA order
arima_order = model.order
# Plot the actual data
# Plot the actual data
fig_actual, ax_actual = plt.subplots()
ax_actual.plot(data, label = data.columns[-1])
ax_actual.set_xlabel(data.index.name)
plt.legend()
ax_actual.set_ylabel(data.columns[-1])
ax_actual.set_title("Plot of Actual data for {}".format(data.columns[-1]))
plt.show()
# Get the last date in the actual data
last_date = data.index[-1]
# Make predictions
predicted_values = model.predict(n_periods=12)
# Generate a range of dates starting from the start date
pred_index = pd.date_range(start=last_date, periods=len(predicted_values)+1, freq="MS")[1:]
# Create a new dataframe with the predicted values and the generated dates
predictions = pd.DataFrame({'predicted_values': predicted_values}, index=pred_index)
predictions.columns = data.columns
predictions.index.name = data.index.name
predictions.index.freq = data.index.freq
# Merge the dataframes using the index
merged_data = pd.concat([data, predictions], axis=0)
num_actual = len(data.index)
# Plot the actual vs predicted data
actual_data = merged_data.iloc[:num_actual,:]
fig, ax = plt.subplots()
ax.plot(actual_data.index, actual_data[data.columns[-1]], label='Actual')
# Plot the predicted data
predicted_data = merged_data.iloc[num_actual:,:]
ax.plot(predicted_data.index, predicted_data[data.columns[-1]], label='Predicted')
# Add x and y axis labels
ax.set_xlabel(data.index.name)
ax.set_ylabel(data.columns[-1])
# Add title and legend
ax.set_title('Plot of Actual and Predicted Values')
ax.legend()
plt.show()
return data.head(), fig_actual, arima_order, predictions, fig
input_data = gr.inputs.File(label="Upload CSV file")
outputs = [gr.outputs.Dataframe(type = "pandas", label = "FIRST FIVE ROWS OF DATASET"),
'plot',
gr.outputs.Textbox(label = "ARIMA ORDER"),
gr.outputs.Dataframe(type = "pandas", label = "PREDICTIONS FOR NEXT 12 PERIODS"),
'plot'
]
examples = ["Electric_Production.csv"]
interface = gr.Interface(
fn=predict_timeseries, inputs=input_data, outputs=outputs,
title="Time series Forecast using AUTO ARIMA",
description="Upload a CSV file of monthly time series data to generate 12 period forecasts using ARIMA.",
theme = 'darkhuggingface',
examples = examples,
live = True)
interface.launch()
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