Time Series Forecast using CNN and LSTM

This repository explores different approaches for time series forecasting using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The goal is to provide a comprehensive understanding of various techniques and their applicability in forecasting time series data.

Sections

1. CNN multi-channel
2. CNN multi-Step
3. Encoder-Decoder LSTM
4. Multi-Headed CNN
5. Univariate LSTM

1. CNN Multi-Channel

The CNN Multi-Channel approach leverages convolutional layers with multiple input channels to capture spatial and temporal patterns in time series data. By applying filters across different channels, the model can extract diverse features and improve forecasting accuracy.

2. CNN Multi-Step

The CNN Multi-Step technique focuses on forecasting multiple future time steps simultaneously. By utilizing convolutional layers to capture temporal dependencies, the model can make predictions for multiple future time points, enhancing the efficiency and accuracy of the forecasting process.

3. Encoder-Decoder LSTM

The Encoder-Decoder LSTM architecture employs a combination of LSTM layers to capture sequential patterns in the input time series data. The encoder part learns representations of the input sequence, while the decoder part generates future predictions based on the encoded information. This approach is effective for capturing long-term dependencies and generating accurate forecasts.

4. Multi-Headed CNN

The Multi-Headed CNN technique involves using multiple convolutional layers with different filter sizes and kernel widths. Each convolutional layer captures different patterns and features from the input time series. By combining the outputs of these layers, the model can obtain a more comprehensive representation of the data, leading to improved forecasting performance.

5. Univariate LSTM

The Univariate LSTM approach focuses on forecasting time series data with a single variable. It utilizes LSTM layers to capture sequential patterns and dependencies in the input data, allowing the model to generate accurate predictions for future time points based on past observations. This technique is suitable for datasets where only one variable is available for forecasting.

Conclusion

This repository provides implementations and explanations of various approaches for time series forecasting using CNN and LSTM models. By exploring different techniques, one can gain insights into the strengths and limitations of each method and choose the most appropriate approach for their specific time series forecasting tasks.