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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Random Forest for Time Series Forecasting

T
Tanushree Last Updated : 26 Feb, 2024
6 min read

Introduction

Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It is an ensemble learning method that uses bagging (bootstrap sample), constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees. It can be used for both Classification and Regression problems in ML. However, it can also be used in time series analysis and forecasting, both univariate and multivariate dataset by creating lag variables and seasonality variables manually. When evaluating the performance of a Random Forest model, metrics such as standard deviation, var, moving average, and linear regression can provide insights into its effectiveness.

No algorithm works best for all the datasets. So depending on the data you can try various algorithms and choose the best for your data. I have tried various time series models ARIMA model, SARIMA, ets, lstm (deep learning), Random forest, XGBoost, and fbprophet for time series forecasting and each of these algorithms worked best for one category or the other. Random forest model, XGBoost, and fbprophet outperformed for multivariate and intermittent data.

Learning Objectives

After completing this tutorial, you will know:

  • How to develop a Random Forest model for univariate/multivariate time series data.
  • How to limit the number of independent variables to a certain value.
  • How to forecast for multiple date points e.g. for the coming 4 months or 4 weeks.

Intermittent data

Intermittent demand data is one of the data types with a very random pattern, for example, demand data. The data will have a value (not zero) if there is a demand. If there is no demand, the data is zero. Intermittent demand data is usually called customer demand data or sales data for an item that is not sold every time.

In this tutorial, you will learn how to develop a Random forest model for time series forecasting.

Let’s get started.

Problem: Forecast demand for a jeans brand for the coming 6 months.

Data: We have monthly sales quantity available for 2 years (from May 2019 to May 2021) in the CSV file.

Import all required Packages

import pandas as pd
from sklearn.feature_selection import RFE
# import random forest regression from scikit-learn to perform regression model
from sklearn.ensemble import RandomForestRegressor
from pandas import DataFrame
import numpy as np
from datetime import timedelta
import calender
jeans_data=pd.read_csv('jeans_data.csv')
jeans_data.head()

Check if the data is stationary

from statsmodels.tsa.stattools import adfuller
from numpy import log
result = adfuller(df.value.dropna())
print('p-value: %f' % result[1])

p-value: 0.024419

Since the p-value is below 0.05, the data can be assumed to be stationary hence we can proceed with the data without any transformation.

Create lag variables

dataframe = DataFrame()
for i in range(12, 0, -1):
   dataframe['t-' + str(i)] = jeans_data.SaleQty.shift(i)
final_data = pd.concat(jeans_data, dataframe], axis=1)
final_data.dropna(inplace=True)

You can give any value in place of 12, depending on your time interval and the number of lags you want to create. It is ideal to give 12 for monthly data and 54 for weekly data and limit the number of independent variables later. These lag variables are used for autoregressive prediction.

Add seasonal variable

Create a variable that has different values for different months which will add a seasonal component to the model, which may help improve the forecast.

final_data['date'] = pd.to_datetime(final_data['date'], format='%Y-%m-%d')
final_data['month'] = final_data['date'].dt.month

Or we can add dummy variables for each month:

dummy = pd.get_dummies(final_data['month'])
final_data = pd.concat([final_data, dummy], axis=1)

Train the model

We will take the most recent 6 months’ subset of data as the test data and the rest of the data as the training dataset.

finaldf = final_data.drop(['date'], axis=1)
finaldf = finaldf.reset_index(drop=True)
test_length=6
end_point = len(finaldf)
x = end_point - test_length
finaldf_train = finaldf.loc[:x - 1, :]
finaldf_test = finaldf.loc[x:, :]
finaldf_test_x = finaldf_test.loc[:, finaldf_test.columns != 'SaleQty']
finaldf_test_y = finaldf_test['SaleQty']
finaldf_train_x = finaldf_train.loc[:, finaldf_train.columns != 'SaleQty']
finaldf_train_y = finaldf_train['SaleQty']
print("Starting model train..")
rfe = RFE(RandomForestRegressor(n_estimators=100, random_state=1), 4)
fit = rfe.fit(finaldf_train_x, finaldf_train_y)
y_pred = fit.predict(finaldf_test_x)

I have used RFE (recursive feature elimination) to limit the number of independent variables/features to 4, you can change the value and choose the value that gives the least error. I have taken n_estimators (number of trees in the forest) 100 which is the default value. We can also try different hyperparameters.

Evaluating the Algorithm

y_true = np.array(finaldf_test_ y['SaleQty'])
sumvalue=np.sum(y_true)
mape=np.sum(np.abs((y_true - y_pred)))/sumvalue*100
accuracy=100-mape
print('Accuracy:', round(accuracy,2),'%.')
# we can also use other metrics like mae or rmse etc.

Accuracy: 89.42 %.

Predict for Future

We will predict sale quantity for the future 6 months. The lags will be null for future date points so we have to predict for one month at a time and use the predicted sale for creating lag for next month’s prediction and so on. Please note we are using the predicted sale only to create the lag variable, we are not training the model again.

# predictive modelling
def create_lag(df3):
    dataframe = DataFrame()
    for i in range(12, 0, -1):
        dataframe['t-' + str(i)] = df3.SaleQty.shift(i)
    df4 = pd.concat([df3, dataframe], axis=1)
    df4.dropna(inplace=True)
    return df4
yhat=[]
future_dataframe= jeans_data.copy()
n=6
x = future_dataframe.at[end_point - 1, 'date']
days_in_month=calendar.monthrange(x.year, x.month)[1]
for i in range(n):
       future_dataframe.at[future_dataframe.index[end_point + i], 'date'] = x + timedelta(days=days_in_month + days_in_month * i)
       future_dataframe.at[future_dataframe.index[end_point + i], SaleQty] = 0
future_dataframe ['date'] = pd.to_datetime(future_dataframe ['date'], format='%Y-%m-%d')
future_dataframe ['month'] = future_dataframe ['date'].dt.month
future_dataframe = future_dataframe.drop(['date'], axis=1)
future_dataframe _end = len(jeans_data)
for i in range(n, 0, -1):
    y = future_dataframe _end - i
    inputfile = finaldf.loc[y:end_point, :]
    inputfile_x = inputfile.loc[:, inputfile.columns != 'SaleQty']
    pred_set = inputfile_x.head(1)
    pred = fit.predict(pred_set)
    future_dataframe.at[future_dataframe.index[future_dataframe _end - i], 'SaleQty'] = pred[0]
    finaldf = create_lag(future_dataframe)
    finaldf = finaldf.reset_index(drop=True)
    yhat.append(pred)
predicted_value= np.array(yhat)

You can add any other independent variables available like promotions, special_days, weekends, start_of_month, etc.

Find below the complete code:

import pandas as pd
from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestRegressor
from pandas import DataFrame
import numpy as np
from datetime import datetime
import calendar
from datetime import timedelta
import datetime as dt
def add_month(df, forecast_length, forecast_period):
    end_point = len(df)
    df1 = pd.DataFrame(index=range(forecast_length), columns=range(2))
    df1.columns = ['SaleQty', 'date']
    df = df.append(df1)
    df = df.reset_index(drop=True)
    x = df.at[end_point - 1, 'date']
    x = pd.to_datetime(x, format='%Y-%m-%d')
    days_in_month=calendar.monthrange(x.year, x.month)[1]
    if forecast_period == 'Week':
        for i in range(forecast_length):
            df.at[df.index[end_point + i], 'date'] = x + timedelta(days=7 + 7 * i)
            df.at[df.index[end_point + i], 'SaleQty'] = 0
    elif forecast_period == 'Month':
        for i in range(forecast_length):
            df.at[df.index[end_point + i], 'date'] = x + timedelta(days=days_in_month + days_in_month * i)
            df.at[df.index[end_point + i], 'SaleQty'] = 0
    df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
    df['month'] = df['date'].dt.month
    df = df.drop(['date'], axis=1)
    return df
def create_lag(df3):
    dataframe = DataFrame()
    for i in range(12, 0, -1):
        dataframe['t-' + str(i)] = df3.SaleQty.shift(i)
    df4 = pd.concat([df3, dataframe], axis=1)
    df4.dropna(inplace=True)
    return df4
def randomForest(df1, forecast_length, forecast_period):
    df3 = df1[['SaleQty', 'date']]
    df3 = add_month(df3, forecast_length, forecast_period)
    finaldf = create_lag(df3)
    finaldf = finaldf.reset_index(drop=True)
    n = forecast_length
    end_point = len(finaldf)
    x = end_point - n
    finaldf_train = finaldf.loc[:x - 1, :]
    finaldf_train_x = finaldf_train.loc[:, finaldf_train.columns != 'SaleQty']
    finaldf_train_y = finaldf_train['SaleQty']
    print("Starting model train..")
    rfe = RFE(RandomForestRegressor(n_estimators=100, random_state=1), 4)
    fit = rfe.fit(finaldf_train_x, finaldf_train_y)
    print("Model train completed..")
    print("Creating forecasted set..")
    yhat = []
    end_point = len(finaldf)
    n = forecast_length
    df3_end = len(df3)
    for i in range(n, 0, -1):
        y = end_point - i
        inputfile = finaldf.loc[y:end_point, :]
        inputfile_x = inputfile.loc[:, inputfile.columns != 'SaleQty']
        pred_set = inputfile_x.head(1)
        pred = fit.predict(pred_set)
        df3.at[df3.index[df3_end - i], 'SaleQty'] = pred[0]
        finaldf = create_lag(df3)
        finaldf = finaldf.reset_index(drop=True)
        yhat.append(pred)
    yhat = np.array(yhat)
    print("Forecast complete..")
    return yhat
predicted_value=randomForest(jeans_data, 6, 'Month')

The random forest ensemble learning method performs bootstrapping of observations by randomly sampling the training set. So the order of the data points change hence it might not perform well in many time series data, but it does perform well for intermittent data as it catches the probability of demand/sale of a zero selling product well.

Please let me know your queries and suggestions if any.

Conclusion

This study conducted an analysis of intermittent data utilizing a specific model. Initially, essential packages were imported, and data stationarity was examined. Lag and seasonal variables were subsequently generated. The model underwent training and evaluation, demonstrating satisfactory results. Additionally, predictions for future data points were generated. This study underscores the significance of meticulous data preparation and the selection of appropriate modeling techniques for intermittent data. Future research avenues may explore alternative models or incorporate additional variables to enhance predictive accuracy. It’s important to note that the actual conclusion may differ based on the study’s specific results and discoveries.

Key Takeaways

  • Using Python for time series forecasting with random forest regression involves careful model fitting to avoid overfitting, ensuring the model generalizes well to unseen data.
  • Validation of the random forest model’s performance on a test set is crucial, employing metrics like mean absolute error to quantitatively assess accuracy.
  • Incorporating a lag and seasonal variables as features in random forest for time series can enhance model performance by capturing trends and reducing noise in the prediction process.

Frequently Asked Questions

Q1. Can random forest be used for forecasting?

A. In data science, the random forest algorithm can be adapted for time series prediction by using lagged observations as predictors. This approach, which involves creating a supervised learning task from univariate time series data, leverages the algorithm’s capacity for handling complex, non-linear relationships.

Q2. What are time series forecasting methods?

A. Time series forecasting methods predict future data points by analyzing historical trends. These include ARIMA for stationary series, SARIMA for seasonal data, Exponential Smoothing for trends and seasonality, Prophet for daily patterns, and machine learning models like Random Forests and Neural Networks.

Q3. What are the advantages and disadvantages for a random forest algorithm?

A. Advantages: Handles non-linear data well, robust to overfitting, and performs feature selection. Disadvantages: Complex and resource-intensive, difficult to interpret, and not ideal for very high-dimensional sparse data.

T
Tanushree

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Responses From Readers

Clear

Chung Lee
Chung Lee

Thank you for sharing this. Where can I downlload the data you used? I'd like to run it with diffenet algorithm.

123

Chung Lee
Chung Lee

Thank you for sharing this. Could you let me know where is the data? I'd like to run it with different algorithm.

123

Tanguy
Tanguy

Nice post: thank you! Question : at the beginning of your article, you state that "Random forest, XGBoost, and fbprophet outperformed for multivariate and intermittent data". However, I do not remind that fbprophet works for multivariate data (only univariate data). So how did you manage to use it with explanatory variables?

123

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