📚 Time Series Analysis with Python Cookbook (2022)

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📚 Time Series Indexing (2023)

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📚 Modern Time Series Forecasting with Python (2022)

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📚 Time Series Analysis on AWS (2023)

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📚 Time Series Forecasting using Python (2022)

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📚 An Overview of Practical Time Series Forecasting Using Python (2024)

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Topic: RNN (Recurrent Neural Networks) – Part 1 of 4: Introduction and Core Concepts

---

1. What is an RNN?

• A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data, such as time series, text, or speech.

• Unlike feedforward networks, RNNs maintain a memory of previous inputs using hidden states, which makes them powerful for tasks with temporal dependencies.

---

2. How RNNs Work

• RNNs process one element of the sequence at a time while maintaining an internal hidden state.

• The hidden state is updated at each time step and used along with the current input to predict the next output.

$$
h_t = \tanh(W_h h_{t-1} + W_x x_t + b)
$$

Where:

• $x_t$ = input at time step t
• $h_t$ = hidden state at time t
• $W_h, W_x$ = weight matrices
• $b$ = bias

---

3. Applications of RNNs

• Text classification
• Language modeling
• Sentiment analysis
• Time-series prediction
• Speech recognition
• Machine translation

---

4. Basic RNN Architecture

• Input layer: Sequence of data (e.g., words or time points)

• Recurrent layer: Applies the same weights across all time steps

• Output layer: Generates prediction (either per time step or overall)

---

5. Simple RNN Example in PyTorch

import torch
import torch.nn as nn

class BasicRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(BasicRNN, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.rnn(x)  # out: [batch, seq_len, hidden]
        out = self.fc(out[:, -1, :])  # Take the output from last time step
        return out

---

6. Summary

• RNNs are effective for sequential data due to their internal memory.

• Unlike CNNs or FFNs, RNNs take time dependency into account.

• PyTorch offers built-in RNN modules for easy implementation.

---

Exercise

• Build an RNN to predict the next character in a short string of text (e.g., “hello”).

---

#RNN #DeepLearning #SequentialData #TimeSeries #NLP

https://t.iss.one/DataScienceM

Topic: RNN (Recurrent Neural Networks) – Part 2 of 4: Types of RNNs and Architectural Variants

---

1. Vanilla RNN – Limitations

• Standard (vanilla) RNNs suffer from vanishing gradients and short-term memory.

• As sequences get longer, it becomes difficult for the model to retain long-term dependencies.

---

2. Types of RNN Architectures

• One-to-One
Example: Image Classification
A single input and a single output.

• One-to-Many
Example: Image Captioning
A single input leads to a sequence of outputs.

• Many-to-One
Example: Sentiment Analysis
A sequence of inputs gives one output (e.g., sentiment score).

• Many-to-Many
Example: Machine Translation
A sequence of inputs maps to a sequence of outputs.

---

3. Bidirectional RNNs (BiRNNs)

• Process the input sequence in both forward and backward directions.

• Allow the model to understand context from both past and future.

nn.RNN(input_size, hidden_size, bidirectional=True)

---

4. Deep RNNs (Stacked RNNs)

• Multiple RNN layers stacked on top of each other.

• Capture more complex temporal patterns.

nn.RNN(input_size, hidden_size, num_layers=2)

---

5. RNN with Different Output Strategies

• Last Hidden State Only:
Use the final output for classification/regression.

• All Hidden States:
Use all time-step outputs, useful in sequence-to-sequence models.

---

6. Example: Many-to-One RNN in PyTorch

import torch.nn as nn

class SentimentRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SentimentRNN, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.rnn(x)
        final_out = out[:, -1, :]  # Get the last time-step output
        return self.fc(final_out)

---

7. Summary

• RNNs can be adapted for different tasks: one-to-many, many-to-one, etc.

• Bidirectional and stacked RNNs enhance performance by capturing richer patterns.

• It's important to choose the right architecture based on the sequence problem.

---

Exercise

• Modify the RNN model to use bidirectional layers and evaluate its performance on a text classification dataset.

---

#RNN #BidirectionalRNN #DeepLearning #TimeSeries #NLP

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Topic: Handling Datasets of All Types – Part 5 of 5: Working with Time Series and Tabular Data

---

1. Understanding Time Series Data

• Time series data is a sequence of data points collected over time intervals.

• Examples: stock prices, weather data, sensor readings.

---

2. Loading and Exploring Time Series Data

import pandas as pd

df = pd.read_csv('time_series.csv', parse_dates=['date'], index_col='date')
print(df.head())

---

3. Key Time Series Concepts

• Trend: Long-term increase or decrease in data.

• Seasonality: Repeating patterns at regular intervals.

• Noise: Random variations.

---

4. Preprocessing Time Series

• Handle missing data using forward/backward fill.

df.fillna(method='ffill', inplace=True)

• Resample data to different frequencies (daily, monthly).

df_resampled = df.resample('M').mean()

---

5. Working with Tabular Data

• Tabular data consists of rows (samples) and columns (features).

• Often requires handling missing values, encoding categorical variables, and scaling features (covered in previous parts).

---

6. Summary

• Time series data requires special preprocessing due to temporal order.

• Tabular data is the most common format, needing cleaning and feature engineering.

---

Exercise

• Load a time series dataset, fill missing values, and resample it monthly.

• For tabular data, encode categorical variables and scale numerical features.

---

#TimeSeries #TabularData #DataScience #MachineLearning #Python

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📌 Introducing ShaTS: A Shapley-Based Method for Time-Series Models

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-17 | ⏱️ Read time: 9 min read

Explaining time-series models with standard tabular Shapley methods can be misleading as they ignore crucial temporal dependencies. A new method, ShaTS (Shapley-based Time-Series), is introduced to solve this problem. Specifically designed for sequential data, ShaTS provides more accurate and reliable interpretations for time-series model predictions, addressing a critical gap in explainable AI for this data type.

#ExplainableAI #TimeSeries #ShapleyValues #MachineLearning

📌 Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair)

🗂 Category: DATA VISUALIZATION

🕒 Date: 2025-11-20 | ⏱️ Read time: 12 min read

Master time-series data visualization in Python with this in-depth guide. The article offers a practical exploration of plotting temporal data, complete with detailed code examples. Learn how to effectively leverage popular libraries like Matplotlib, Plotly, and Altair to create insightful and compelling visualizations for your data science projects.

#DataVisualization #Python #TimeSeries #DataScience