Leveraging Prophet Model for Daily Expense and Income Time Series Forecasting at Argyle Enigma Tech Labs

In today's data-driven world, accurate forecasting is crucial for businesses, organizations, and individuals alike. Whether you're predicting sales, website traffic, or any other time-dependent phenomena, having a reliable forecasting model can greatly improve your decision-making process. Enter Prophet, a powerful and intuitive time series forecasting tool developed by Facebook's Core Data Science team.

What is Prophet Model?

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.

Prophet can be considered a nonlinear regression model, of the form

Yt = g(t) + s(t)+ h(t) + εt,

were

g(t) describes a piecewise-linear trend (or “growth term”),

s(t) describes the various seasonal patterns, 

h(t) captures the holiday effects, and 

εt is a white noise error term.

• The knots (or changepoints) for the piecewise-linear trend are automatically selected if not explicitly specified. Optionally, a logistic function can be used to set an upper bound on the trend.

• The seasonal component consists of Fourier terms of the relevant periods. By default, order 10 is used for annual seasonality and order 3 is used for weekly seasonality.

• Holiday effects are added as simple dummy variables.

• The model is estimated using a Bayesian approach to allow for automatic selection of the changepoints and other model characteristics.

Leveraging Prophet Model

 How does Prophet Model work?

At its core, Prophet is an additive regression model that decomposes a time series into four main components: trend, seasonality, holidays, and errors. Let's briefly explore each component:

1.     Trend: The overall trend or growth curve in the data, which can be linear or non-linear.

2.     Seasonality: Periodic patterns that repeat over a fixed period, such as weekly, monthly, or yearly cycles.

3.     Holidays: Specific dates that may cause deviations from the normal pattern, such as holidays or special events.

4.     Errors: Residuals or unexplained noise in the data that cannot be captured by the other components.

By combining these components, Prophet can accurately model and forecast time series data with varying levels of complexity.

Prophet Structure

Advantages and Limitations of Prophet Model.

Advantages: 

• Easy to use and intuitive interface.

• Automatic detection and modeling of seasonal patterns.

• Ability to incorporate holidays and special events.

• Flexible trend modeling (linear or non-linear).

• Provides prediction intervals and uncertainty estimates.

• Open-source and well-documented.

Limitations:

• Limited to univariate time series forecasting.

• Assumptions of additive components and continuity of time series

• Difficulty in handling complex or abrupt changes in patterns.

• Limited control over model internals and hyperparameters.

• Performance may degrade for very long-term forecasts.

Argyle Enigma Tech Labs Used Case: Time Series Forecasting on Expenses using Prophet Model.

Problem Statement: To develop accurate and reliable time series forecasting models for daily expenses and income data using Prophet Model.

1.     Data Collection: The daily expenses and income dataset was collected from the user's day-to-day transactions spanning from January 2018 to February 2024. The dataset included the following variables: 'Date,' 'Time,' 'Expense Amount,' and 'Category.'

2.     Data Preprocessing: The steps involved in data preprocessing are:

• Converting the 'Date' and 'Time' columns into Datetime format.

• Dropping all unnecessary variables.

• Plotting the time series to check the trend and seasonality.

• Identifying outliers and performing necessary imputations.

• Resampling the data to daily frequencies.

      By completing these steps, the data is prepared for model fitting.

3.     Model Fitting:

• Renaming the 'Datetime' column as 'ds' and the 'Amount' column as 'y' is a prerequisite for fitting the Prophet Model.

• Initializing the Prophet Model to forecast 'Expense Amount' ('y') for 12 months ahead with hourly frequency.

• Evaluating the model's goodness of fit using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The objective is to minimize these values to ensure successful model fitting.

• Plotting the forecasted values against the actual values to visualize the output.

• Deployment of the Prophet Model using Flask: The fitted model is deployed using Flask to provide a better user interface.

Conclusion

Prophet's ease of use and robust capabilities make it a valuable tool for time series forecasting, enabling businesses and individuals to make informed decisions based on accurate predictions. At Argyle Enigma Tech Labs, our use case on forecasting daily expenses demonstrates the practical applications and effectiveness of Prophet in real-world scenarios. By leveraging Prophet, we can confidently navigate the complexities of time-dependent data and uncover actionable insights to drive future success. Whether you're a seasoned data scientist or a business professional, Prophet empowers you to harness the power of forecasting with confidence and precision.

Leveraging LDA and NLP for Topic Modeling: Thematic Insights from Community Comments at Argyle Enigma Tech Labs.

In the vast ocean of textual data, making sense of the content can be a daunting task. Whether you're dealing with social media posts, research articles, or customer reviews, uncovering the hidden themes and structures within this data is crucial. This is where topic modeling comes into play, and one of the most popular and effective algorithms for this task is the Latent Dirichlet Allocation (LDA) model. 

What is Latent Dirichlet Allocation (LDA)?

Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document.

Topic modeling is a way of abstract modeling to discover the abstract ‘topics’ that occur in the collections of documents. The idea is that we will perform unsupervised classification on different documents, which find some natural groups in topics.

The basic idea is that documents are represented as a random mixture over latent topics, where a Dirichlet distribution over words characterizes each topic to find topics in documents, or LDA identifies a set of topics by associating a set of words to each topic. The underlying assumption of LDA is that a text document will consist of multiple themes and has a three-level hierarchical Bayesian model where each item of a collection of text is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of word probabilities.

How does the LDA Model works?

The LDA model is based on the following key concepts:

1.     Documents as Mixtures of Topics:

Each document is represented as a mixture of several topics. For instance, a news article might cover topics like politics, economics, and technology.

2.     Topics as Distributions over Words:

Each topic is represented as a distribution over a fixed vocabulary of words. For example, a topic on technology might have words like "computer," "software," and "internet" with varying probabilities.

3.     Dirichlet Distribution:

LDA uses Dirichlet distributions as priors for the per-document topic distributions and the per-topic word distributions. The Dirichlet distribution is a family of continuous multivariate probability distributions parameterized by a vector of positive reals.

4.     The LDA Generative Process:

The generative process of LDA can be summarized in the following steps:

• Choose a topic distribution for each document from a Dirichlet prior.

• Choose a word distribution for each topic from a Dirichlet prior.

• For each word in a document: Select a topic from the document's topic distribution, Select a word from the topic's word distribution.

5.     Inference in LDA:

The core challenge in LDA is the inference problem: given a collection of documents, how can we infer the topic distributions for each document and the word distributions for each topic? This is typically done using approximate inference techniques such as:

• Variational Inference: This approach approximates the posterior distribution of the hidden variables (topics) by optimizing a lower bound on the log-likelihood of the observed data.

• Gibbs Sampling: This is a Markov Chain Monte Carlo (MCMC) method used to sample from the posterior distribution of the hidden variables.

Applications of LDA Model

LDA has a wide range of applications across various domains:

1.     Topic Modeling: LDA is primarily used for topic modeling to discover the underlying themes in large document collections. This is useful in areas like content recommendation, document classification, and summarization.

2.     Information Retrieval: LDA can enhance information retrieval systems by improving the accuracy of search results based on topic relevance rather than keyword matching alone.

3.     Social Media Analysis: LDA is used to analyze social media data to identify trending topics, sentiment analysis, and community detection.

4.     Marketing and Customer Insights: Businesses use LDA to gain insights into customer feedback, reviews, and surveys to understand customer preferences and sentiments.

Limitations of LDA Model

While LDA is a powerful tool, it has its limitations and challenges:

1.     Scalability: LDA can be computationally intensive, especially for large datasets. Efficient parallel and distributed implementations are required for scalability.

2.     Number of Topics: Choosing the optimal number of topics is often challenging and can significantly impact the quality of the results.

3.     Interpretability: Interpreting the topics generated by LDA can be subjective and may require domain expertise to label and understand the topics accurately.

4.     Assumptions: LDA assumes that topics are static and do not change over time, which may not hold true for dynamic datasets like social media streams.

Argyle Enigma Tech Labs Used Case: Topic Modeling on Community Comments.

Problem Statement: Leveraging natural language processing (NLP) techniques and the LDA Model for Topic Modeling on community comments.

1.     Import Libraries:

• pandas: Used for data manipulation and analysis.

• re: Used for regular expression operations.

• nltk: The Natural Language Toolkit, used for various text processing tasks.

2.     Load Dataset: The dataset containing community comments will be loaded into Pandas Data Frame.

3.     Download NLTK Resources: Essential NLTK resources are downloaded:

• punkt: Tokenizer models.

• stopwords: Common stop words for multiple languages.

• wordnet: Lexical database for English.

4.     Initialize WordNet Lemmatizer: The WordNet Lemmatizer is initialized to reduce words to their base or root form.

5.     Preprocess Comments: Each comment undergoes several preprocessing steps:

• Non-alphabetic characters are removed.

• The text is converted to lowercase.

• The text is tokenized into words.

• Words are lemmatized and stop words are removed.

• The processed words are rejoined into a single string.

• The cleaned and preprocessed comments are stored in a list called ‘corpus’.

6.     Topic Modeling:

• Importing Libraries: ‘gensim’(A library for topic modeling and document similarity analysis), ‘corpora’( A Gensim module for creating and working with document-term matrices), ‘models’( A Gensim module containing various algorithms, including LDA (Latent Dirichlet Allocation)), ‘word_tokenize’ (Tokenizes the text into individual words).

• Tokenizing the Corpus: Tokenizes each cleaned comment into words.

• Ensuring Corpus as List of Strings: Joins tokenized words back into strings to ensure the corpus is in the correct format.

• Creating Dictionary and Corpus: Dictionary: Maps each unique word to an ID, doc2bow: Converts each document into a bag-of-words format (list of tuples with word ID and frequency).

• Building the LDA Model: ‘Lda_Model’, trains the LDA model with the specified number of topics (19 in this case) using the dictionary and corpus.

• Printing the topics: Prints the topics discovered by the LDA model along with the top words in each topic.

Conclusion

The Latent Dirichlet Allocation (LDA) model is a pivotal tool for topic modeling, enabling the discovery of hidden themes in textual data. Despite challenges like scalability and interpretability, LDA finds diverse applications in content recommendation, social media analysis, and customer insights. At Argyle Enigma Tech Labs, we've harnessed LDA for topic modeling on community comments, underscoring its significance in extracting actionable insights from unstructured text.