when entering

pip install fbprophet

maybe you get this error:

ModuleNotFoundError: No module named 'pystan'
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure

× Encountered error while trying to install package.
╰─> fbprophet

note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.

or any others and you tried everything!!
just try this one and do not install conda or anything else!!:

pip install prophet 

or

pip install pandas fbprophet

then start coding easily:

you can add news data as an additional regressor to the model by using the add_regressor method in the prophet library.

Here’s an example code:

import pandas as pd
from fbprophet import Prophet

# Load the exchange rate and news data into separate pandas dataframes
df_exchange = pd.read_csv("dollar_exchange_rate.csv")
df_news = pd.read_csv("news_data.csv")

# Merge the two dataframes into a single dataframe
df = pd.merge(df_exchange, df_news, on='date', how='left')

# Rename the columns to meet the requirement of prophet
df = df.rename(columns={"date": "ds", "rate": "y"})

# Initialize the prophet model
model = Prophet()

# Add the news data as an additional regressor
model.add_regressor("news_sentiment")

# Fit the model to the combined exchange rate and news data
model.fit(df)

# Define the number of future days to forecast
future = model.make_future_dataframe(periods=365)

# Forecast the future exchange rates
forecast = model.predict(future)

# Plot the forecast
model.plot(forecast)

Note: In this example, the exchange rate and news data are assumed to be in separate csv files named dollar_exchange_rate.csv and news_data.csv respectively. The news_data.csv file should contain the date and a measure of news sentiment (such as the number of positive news articles minus the number of negative news articles) for each date. You will need to modify the code to reflect the actual data you are using.

Here is a sample code to forecast the weather of Tehran using the ARIMA algorithm and the statsmodels library:

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error

# Load the weather data for Tehran into a Pandas dataframe
data = pd.read_csv("tehran_weather.csv")

# Convert the data into a time series format
date_rng = pd.date_range(start='1/1/2018', end='12/31/2022', freq='D')
tehran_weather = data.set_index(date_rng)

# Split the data into training and testing sets
train = tehran_weather[:int(0.7*(len(tehran_weather)))]
test = tehran_weather[int(0.7*(len(tehran_weather))):]

# Fit an ARIMA model to the training data
model = ARIMA(train['temperature'], order=(2, 1, 2))
model_fit = model.fit(disp=0)

# Use the model to make predictions on the test data
forecast, stderr, conf_int = model_fit.forecast(steps=len(test), alpha=0.05)

# Calculate the mean squared error of the predictions
mse = mean_squared_error(test['temperature'], forecast)
print("Mean Squared Error: ", mse)

This code assumes that you have a CSV file called tehran_weather.csv containing the temperature data for Tehran. The code uses the ARIMA model from the statsmodels library to fit the data and make predictions. The mean squared error of the predictions is then calculated and printed.

Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are commonly used metrics to evaluate the performance of a time series forecasting model. These metrics compare the predicted values with the actual values and provide information on the accuracy of the forecasts.

1. Mean Squared Error (MSE): The MSE is the average of the squared differences between the actual and predicted values. It measures the average squared deviation of the predictions from the actual values. The larger the value of MSE, the larger the deviation, and the worse the model’s performance.
2. Mean Absolute Error (MAE): The MAE is the average of the absolute differences between the actual and predicted values. It measures the average deviation of the predictions from the actual values. The larger the value of MAE, the larger the deviation, and the worse the model’s performance.
3. Root Mean Squared Error (RMSE): The RMSE is the square root of the MSE. It measures the root-mean-square deviation of the predictions from the actual values. Like MSE, the larger the value of RMSE, the larger the deviation, and the worse the model’s performance.

These metrics help to evaluate the overall accuracy of a model’s predictions and can be used to compare different models or to compare the results of different time series forecasting techniques. When selecting a time series forecasting model, it’s important to choose a metric that is most appropriate for your problem, depending on the nature of your data and the goals of your analysis.