Hello everyone !!!

Why time series analysis?
Time series analysis is a branch of statistics and econometrics dedicated to the study of data collected at regular intervals over time. The aim is to understand the underlying structure of a time series, to model its behavior, and to make forecasts based on this data. Here's why this analysis is important: Detecting trends and patterns, Predicting future events, Analyzing temporal dependencies, Identifying anomalies, Decomposing the time series, Taking into account the dynamics of complex systems, Applications in many fields, Analyzing temporal interactions and Taking into account shocks and structural breaks.

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Why should I help you with time series analysis?

My name is Rodrigue and I'm a mathematical statistician with solid expertise in mathematics and statistics, with 6 years' experience in this field. Indeed, I hold a research master's degree in statistics and probability and a bachelor's degree in fundamental mathematics.

Choosing a skilled time-series data analyst like me is crucial to maximizing the value of data and achieving an organization's objectives. Here are a few reasons why I might be the ideal choice as a data analyst:
1. Solid technical skills
- I have technical skills in data analysis tools such as Python, R, SQL or Power BI.
- I am proficient in time series analysis, which enables me to apply the right analysis methods depending on the type of data and the questions to be answered.
2. Practical experience
- I have experience in previous projects, which proves my ability to process real data and derive actionable insights.
- I probably have concrete examples of your work, showing how my analyses have led to strategic decisions or operational improvements.
3. Understanding Business Needs
- I am able to understand business objectives and relate my analyses to the organization's desired outcomes.
Critical Thinking and Problem Solving
- I have the critical thinking skills to interrogate data, ask the right questions, and solve complex problems.
- I am able to think analytically to identify trends, anomalies and opportunities.
4. Communication skills
- I am able to clearly communicate my findings and recommendations to a non-technical audience, facilitating decision-making within the organization.
- I can present powerful visualizations that make my analyses more accessible and understandable.
5. Passion for Data Analysis
- My enthusiasm for data analysis and my willingness to learn and continually improve are valuable assets.
- I keep abreast of the latest trends and technologies in data analysis, which enables me to bring innovative approaches to bear.
6. Adaptability and flexibility
- I can adapt quickly to changes in organizational priorities or technological developments.
- I am able to work in a dynamic environment, which is essential in the constantly evolving field of data analysis.
7. Ethics and Integrity
- I will adhere to ethical practices in data management and analysis, ensuring protection of sensitive data and compliance with privacy regulations.

Contact me so that we can discuss your problem via chat. That way, if you have any doubts about the methods to be used, I'll make suggestions that you're free to approve or not. The aim is not to increase the number of options to be billed to you, but to help you address all the contours of your research question.

Project rendering :
- Results Interpretation: Analysis of statistical significance (p-value), effect sizes (coefficients), and confidence intervals.
- Tables and Visualizations: Creation of tables or graphs for clear visualization of results.
- Final Report: Presentation of findings in report form, highlighting key relationships, insights gained and strategic recommendations based on the analysis.


Basic offer:
For 10 euros, you get:
-An exploration of the type of univariate time series of up to two variables.
-An exploration of the components of univariate time series of up to two variables, such as trend and seasonality.
-An exploration of the decomposition of univariate time series of up to two variables, such as the additive and multiplicative model.
The results of these analyses will be accompanied by an interpretation of one or 2 sentences at most within 2 days.


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To go further in your time series analyses and make realistic, reliable forecasts, I suggest the following complementary packs:

PACK A: Moving average (MA) model forecast of a univariate time series. (40 euros)
The moving average (MA) model is one of the fundamental models of time series analysis, used to model series where the current observation depends on past random errors (or “white noise”).
Results will be provided within 2 days at most.

PACK B: Autoregressive (AR) model prediction of a univariate time series. (40 euros)
The autoregressive (AR) model is a classic model used in time series analysis, where the current value of the series is expressed as a linear function of the past values of the series itself.
Results will be provided within 2 days at most.

PACK C: Autoregressive Moving Average Model (ARMA) forecast of a univariate time series. (60 euros).
The Autoregressive Moving Average Model (ARMA) is a hybrid model that combines the features of the Autoregressive (AR) and Moving Average (MA) models. It is used to model stationary time series, capturing both the dependence on past series values (AR) and the influence of past errors (MA). The model is suitable for time series presenting both autocorrelations and fluctuations caused by random shocks.
Results will be delivered within 2 days.


PACK D: Autoregressive integrated moving average (ARIMA) model forecast of a univariate time series. (80 euros)
The Autoregressive Integrated Moving Average (ARIMA) model is a generalization of the ARMA model (autoregressive and moving average), which models non-stationary time series by including a differentiation step to eliminate long-term trend or fluctuations. The ARIMA model is used to model time series by predicting future values based on a combination of autoregressive dependencies (AR), moving averages (MA), and differentiation (I for “Integrated”).
Results will be provided within 2 days at most.


PACK E: ARIMA model prediction using hyperparameter adjustment grid search for a univariate time series. (100 euros)
Grid search for hyperparameter tuning of an ARIMA model consists of systematically testing different combinations of hyperparameters
p, 𝑑 and 𝑞 in order to find the best configuration that optimizes fit and forecast quality. This method makes it possible to fit the model to the data more efficiently by searching for the optimal values of these hyperparameters.
Results will be provided within 3 days at most.

PACK F: Seasonal forecasting Autoregressive integrated moving average model (SARIMA) of a univariate time series. (120 euros)
The SARIMA (Seasonal Autoregressive Integrated Moving Average) model is an extension of the ARIMA model that takes seasonality into account in a time series. This model is useful when data show regular seasonal patterns that recur at regular intervals. By adding seasonal components to the autoregressive (AR), integrated (I) and moving average (MA) terms, SARIMA captures periodic behavior and long-term trends.
Results will be provided within 3 days at most.

PACK G: Simple Exponential Smoothing (SES) model forecasting of a univariate time series. (60 euros)
The Simple Exponential Smoothing (SES) model is a forecasting method for univariate time series that estimates future values by weighting past observations with exponential weights. This model is particularly useful for time series with no trend or seasonality, where forecasts are mainly based on past values.
Results will be provided within 2 days at most.

PACK H: Holt-Winters (HW) model forecast of a univariate time series. (60 euros)
The Holt-Winters model, also known as triple exponential smoothing, is an extension of the simple exponential smoothing model, which takes into account both trend and seasonality in univariate time series. This model is particularly suited to series with regular seasonal patterns, incorporating forecasts that vary according to past trends and seasonal cycles.
Results will be delivered within 2 days at most.

PACK I: FBProphète model forecast of a univariate time series. (150 euros)
FBProphet is a library developed by Facebook for forecasting time series. It is particularly effective for handling data with seasonal trends, holidays and other events that can affect values. Prophet is designed to be robust to outliers and can be easily adapted to complex models.
Results will be provided within 4 days at most.


PACK J: FBProphet model prediction by controlling the FBProphet change points of a univariate time series. (150 euros)
The FBProphet model can be used to control change points to adjust the model's flexibility in the face of sudden variations in the trend of time series. Change points are moments when the trend of a time series changes significantly, and FBProphet can detect these changes or allow the user to specify them.
Results will be provided within a maximum of 4 days.



I have technical and theoretical skills in :
-Model prediction FBProphet modeling by adjusting the trends of a univariate time.
-FBProphet model forecast with Holidays of a univariate time series.
-Model forecast Auto-ARIMA model forecast of a univariate time series.
-VAR model forecast of a multivariate time series of up to 15 variables.
--LSTM model forecast of a univariate time series.
-NeuralProphet model forecasting of a univariate time series.
-RNN model forecasting of a univariate time series.
-GRU model forecasting of a multivariate time series.

Do not hesitate to place your order, you will be satisfied, for sure.
If your need is specific or if you do not find yourself in all these options? Do not hesitate to contact me via chat. During our discussions, we will determine the options that suit you, and if necessary, personalized options will be created for you.

See you soon!