How do I predict the near future value correctly in python?


I need help, I m currently deploying my LSTM model in flask python, I m trying to load my result to new csv file, but eventually, it loaded with the repeated result, so I have no idea which line of code was doing wrong, Please adjust me and give me some tips Thanks a lots!

import numpy as np
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from pickle import dump

def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)
# load dataset
# load the dataset
dataframe = read_csv('Sales.csv', usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
train_X, train_Y = create_dataset(train, look_back)
test_X, test_Y = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))

model = Sequential()
model.add(LSTM(128, return_sequences=True ,input_shape=(train_X.shape[1], train_X.shape[2])))

model.compile(optimizer='adam', loss='mse')
history =, train_Y, epochs=100, batch_size=128, validation_data=(test_X, test_Y), verbose=2, shuffle=False)

#save the model'model.h5')

from flask import Flask, make_response, request, render_template
from pandas import DataFrame
import io
from pandas import datetime
from io import StringIO
import csv
import pandas as pd
import numpy as np
import pickle
import os
from keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
import datetime
from datetime import timedelta, datetime
from dateutil.relativedelta import relativedelta

app = Flask(__name__)

def form():
    return """
                <h1>Let's TRY to Predict..</h1>
                <p> Insert your CSV file and then download the Result
                <form action="/transform" method="post" enctype="multipart/form-data">
                    <input type="file" name="data_file" class="btn btn-block"/>
                    <button type="submit" class="btn btn-primary btn-block btn-large">Predict</button>

                 <div class="ct-chart ct-perfect-fourth"></div>


@app.route('/transform', methods=["POST"])
def transform_view():
 if request.method == 'POST':
    f = request.files['data_file']
    if not f:
        return "No file"

    stream = io.StringIO("UTF8"), newline=None)
    csv_input = csv.reader(stream)
    result =
    df = pd.read_csv(StringIO(result), usecols=[1])
    #extract month value
    df2 = pd.read_csv(StringIO(result))
    matrix2 = df2[df2.columns[0]].to_numpy()
    list1 = matrix2.tolist()
    # load the model from disk
    model = load_model('model.h5')
    dataset = df.values
    dataset = dataset.astype('float32')
    scaler = MinMaxScaler(feature_range=(0, 1))
    dataset = scaler.fit_transform(dataset)
    dataset = np.reshape(dataset, (dataset.shape[0], 1, dataset.shape[1]))
    predict = model.predict(dataset)
    transform = scaler.inverse_transform(predict)

    X_FUTURE = 100
    transform = np.array([])
    last = dataset[-1]
    for i in range(X_FUTURE):
        curr_prediction = model.predict(np.array([last]))
        last = np.concatenate([last[1:], curr_prediction])
        transform = np.concatenate([transform, curr_prediction[0]])
    transform = scaler.inverse_transform([transform])[0]

    dicts = []
    curr_date = pd.to_datetime(list1[-1])
    for i in range(X_FUTURE):
        curr_date = curr_date +  relativedelta(month=1)
        dicts.append({'Predictions':transform[i], "Month": curr_date})

    new_data = pd.DataFrame(dicts).set_index("Month")
    ##df_predict = pd.DataFrame(transform, columns=["predicted value"])

    response = make_response(new_data.to_csv(index = True, encoding='utf8'))
    response.headers["Content-Disposition"] = "attachment; filename=result.csv"
    return response

if __name__ == "__main__":, port = 9000, host = "localhost")

This is the result that loaded to the new csv file

enter image description here


I think it is the case that you have correct results (meaning duplicates), your LSTM is trained correctly (but maybe with low accuracy), and duplicates are not a mistake but correct answer.

Regarding duplicate Month column values – the reason is that Pandas can’t recognize relativedelta from dateutil package hence adding it to date gives wrong result. Instead try doing this curr_date = curr_date + pd.DateOffset(months = 1), this will produces correct different dates in your Month column.

Answered By – Arty

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

Leave a Reply

(*) Required, Your email will not be published